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Variations on the theme: focus on cerebellum and emotional processing

May 2023
Frontiers in Systems Neuroscience 17

May 2023
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DOI:10.3389/fnsys.2023.1185752

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Authors:

Dimitri Rodarie

University of Pavia

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The cerebellum operates exploiting a complex modular organization and a unified computational algorithm adapted to different behavioral contexts. Recent observations suggest that the cerebellum is involved not just in motor but also in emotional and cognitive processing. It is therefore critical to identify the specific regional connectivity and microcircuit properties of the emotional cerebellum. Recent studies are highlighting the differential regional localization of genes, molecules, and synaptic mechanisms and microcircuit wiring. However, the impact of these regional differences is not fully understood and will require experimental investigation and computational modeling. This review focuses on the cellular and circuit underpinnings of the cerebellar role in emotion. And since emotion involves an integration of cognitive, somatomotor, and autonomic activity, we elaborate on the tradeoff between segregation and distribution of these three main functions in the cerebellum.

…

Cerebellar microcircuit variants. (A) Canonical circuit of the cerebellum. The primary input of mossy fibers (MF) project to deep cerebellar nuclei (DCN), granule cells (GrC), and Golgi cells (GoC). GoC inhibits GrC in a feedforward and feedback loop. Besides GoC, GrC axons activates Purkinje cell (PC) and molecular layer interneurons (MLI). PCs provide the sole output of the cerebellar cortex and inhibit the cerebellar nuclei. (B) A first variant of the canonical circuit is represented by PCs axon collaterals projecting to GrC in the granular layer. (C) A second variant is represented by unipolar brush cells (UBC), that are contacted by MF and excite GrC. UBC receive inhibition through PC axon collaterals. (D) A third variant shows the nucleo-cortical projections: DCN axon collaterals contact GrCs with excitatory connections and inhibit GoCs, in the granular layer.

…

Neuron density and thickness in the mouse cerebellum based on the Blue Brain Cell Atlas. (A) Estimations of the thickness of the molecular layer in mm for each subregion of the cerebellar cortex, using the algorithm proposed by Rodarie et al. (2022). (B) Estimations of the density of excitatory and inhibitory neurons in the molecular and granular layer for each region of the cerebellar cortex. The data are based on the publicity available Blue Brain Cell Atlas (2018) website.

…

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fnsys-17-1185752 May 3, 2023 Time: 13:42 # 1

TYPE Review

PUBLISHED 10 May 2023

DOI 10.3389/fnsys.2023.1185752

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EDITED BY

Conor J. Houghton,

University of Bristol, United Kingdom

REVIEWED BY

Yongsoo Kim,

Penn State Health Milton S. Hershey Medical

Center, United States

Anders Rasmussen,

Lund University, Sweden

*CORRESPONDENCE

Egidio D’Angelo

dangelo@unipv.it

†These authors share ﬁrst authorship

RECEIVED 13 March 2023

ACCEPTED 18 April 2023

PUBLISHED 10 May 2023

CITATION

Ciapponi C, Li Y, Osorio Becerra DA,

Rodarie D, Casellato C, Mapelli L and

D’Angelo E (2023) Variations on the theme:

focus on cerebellum and emotional

processing.

Front. Syst. Neurosci. 17:1185752.

doi: 10.3389/fnsys.2023.1185752

Casellato, Mapelli and D’Angelo. This is an

open-access article distributed under the terms

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does not comply with these terms.

Variations on the theme: focus on

cerebellum and emotional

processing

Camilla Ciapponi1†, Yuhe Li1†, Dianela A. Osorio Becerra1†,

Dimitri Rodarie1,2 , Claudia Casellato1, Lisa Mapelli1and

Egidio D’Angelo1,3*

1Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, 2Centro Ricerche Enrico

Fermi, Rome, Italy, 3Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy

The cerebellum operates exploiting a complex modular organization and a

uniﬁed computational algorithm adapted to different behavioral contexts. Recent

observations suggest that the cerebellum is involved not just in motor but

also in emotional and cognitive processing. It is therefore critical to identify

the speciﬁc regional connectivity and microcircuit properties of the emotional

cerebellum. Recent studies are highlighting the differential regional localization

of genes, molecules, and synaptic mechanisms and microcircuit wiring. However,

the impact of these regional differences is not fully understood and will require

experimental investigation and computational modeling. This review focuses on

the cellular and circuit underpinnings of the cerebellar role in emotion. And

since emotion involves an integration of cognitive, somatomotor, and autonomic

activity, we elaborate on the tradeoff between segregation and distribution of

these three main functions in the cerebellum.

KEYWORDS

cerebellum, emotion, Purkinje cell, microcircuit functioning, modular organization

Emotion and the cerebellum

The term emotion is used to designate a collection of neurophysiological responses

triggered from parts of the brain to the body and to other parts of the brain, elicited by

stimuli from the external world. A collection of such responses results in an emotional state,

deﬁned by changes within the body. The term feeling is used to describe the complex mental

state that results from the emotional state and reﬂects the ability to subjectively experience

the mental states resulting from the experience of emotions (Damasio, 1998). Several studies

are striving to understand the neurophysiological underpinnings of emotional processing,

intended as the ensemble of mechanisms that allow emotion to be generated, learned, and

managed.

There is consensus that emotion in mammals involves complex brain networks

centered on structures classically referred to as the limbic system (Papez, 1937;

Maclean, 1990;Nakano, 1998). These include a core composed by the cingulate cortex,

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amygdala, hypothalamus, and hippocampus and have been deﬁned

quite circularly by their involvement in emotion (Damasio, 1998).

It has recently been noted that a discrete system of components

may not be suﬃcient to describe emotion (Roxo et al., 2011;

Barrett and Satpute, 2019;Steﬀen et al., 2022) and indeed the

classical networks of the limbic system have been extended to

include the periaqueductal gray (PAG), striatum, prefrontal cortex

and parietal cortex, and cerebellum (Apps and Strata, 2015;

Adamaszek et al., 2017;Rolls, 2019). The cerebellum is typically

recognized for its role in movement coordination and motor

learning, but increasing evidence suggests it may also be involved

in higher-order functions, including emotional and cognitive

processing (Schmahmann, 2004). Over the past three decades,

insights into the role of the cerebellum in emotion have increased

to include perception, recognition, forwarding, encoding, and

learning of emotional information. These processes are thought

to substantially contribute to the generation of experiences and

the regulation of emotional states in relation to motor, cognitive,

and social behaviors, with implications for pain, speech, and mood

disorders.

In humans, neuroimaging indicates cerebellar activation in

fear learning paradigms (Lange et al., 2015;Ernst et al., 2019).

Structural and functional abnormalities also appear to be correlated

with impaired mood regulation and anxiety disorders, generating

what is known as the cerebellar cognitive-aﬀective syndrome

(here, “aﬀective” corresponds to “emotional”) (Hoche et al., 2018).

While investigations in humans are providing a privileged point

of view on emotion, investigations in experimental animals are

revealing most of what we know about the underlying neuronal and

circuit mechanisms. Therefore, we will consider the two aspects in

turn.

This review takes a physiological perspective evaluating the

cellular and circuit underpinnings of the cerebellar role in

emotion. And since emotion involves an integration of cognitive,

somatomotor, and autonomic activity (Itô, 1984;Ito, 1993,

2005, 2008;Schmahmann, 2004;Schmahmann and Caplan, 2006;

D‘Angelo and Casali, 2013;Apps et al., 2018), we will elaborate on

the possible distinction, convergence, and overlap of these three

main functions in the cerebellum.

How could the cerebellum support

emotional processing?

This chapter provides a succinct summary of cerebellar

structure, function, and dynamics, to understand how they

contribute to processing diﬀerent aspects of behavior, and in

particular emotion.

Abbreviations: AA, ascending axon; BC, basket cell; BLA, basolateral

amygdala; CeA, central amygdala; CF, climbing ﬁber; CS, conditioned

stimulus; DCN, deep cerebellar nuclei; FC, fear conditioning; fMRI,

functional magnetic resonance imaging; GoC, Golgi cell; GrC, granule cell;

IO, inferior olive; LA, lateral nucleus of the amygdala; MF, Mossy ﬁber;

MLI, molecular layer interneuron; MRI, magnetic resonance imaging; PAG,

periaqueductal Gray; PC, Purkinje cell; PF, parallel ﬁber; SC, Stellate cell;

UBC, unipolar brush cell; US, unconditioned stimulus; vlPAG, ventrolateral

periaqueductal Gray; VTA, ventral tegmental area; Z-, zebrin II negative; Z+,

zebrin II positive.

Structure

The cerebellum contains a well-deﬁned set of cells and

ﬁbers and is organized in parasagittal modules and transverse

zones, that are further subdivided based on biochemical mapping

(Figure 1). The intersection of these sagittal and transverse maps

generates smaller units called cortical microzones which, once

connected to the deep cerebellar nuclei (DCN) and inferior

olive (IO) neurons (see abbreviation list), form the olivo-cortico-

nuclear microcomplexes (Apps and Hawkes, 2009;Apps et al.,

2018).

The concept of microcomplex is rooted in the speciﬁc

organization of the climbing ﬁbers (CFs) emitted by the IO and

in their projections to Purkinje cells (PCs) and DCN cells (Apps

and Hawkes, 2009;Figure 2). Indeed, CFs ﬁrst contact DCN

cells and then branch on the sagittal plane contacting several

PCs (about 7 in rodents). These PCs in turn project back to

the same DCN cells receiving a common IO input and then

these DCN cells, through inhibitory interneurons, project to the

same IO neurons that generated the CF, forming a closed loop

circuit. Mossy ﬁber (MFs) are the other major inputs to these

circuits. Opposite to CFs, MFs spread on the transverse plane

(some branches can cross the midline reaching the contralateral

cerebellum) and do not respect the borders of microcomplexes.

And parallel ﬁbers (PFs), the axon of granule cells (GrCs), bifurcate

in the molecular layer traveling transversally and connecting

regions several millimeters away. In addition, intrinsic biochemical

properties of PCs generate maps, often referred to as stripes

(Apps and Hawkes, 2009), oriented along the longitudinal axis,

increasing the possibility for MFs to intercept regions with

diﬀerent PC properties. Recently, Zebrin II (Aldolase C) positive

and negative stripes have been identiﬁed (Figure 1). The zebrin

stripes largely overlap with longitudinal cerebellar modules to

generate microzones (Apps and Hawkes, 2009) and PCs within

a microzone show synchronous ﬁring when activated by the IO

loops through the CFs (Witter and De Zeeuw, 2015). Functionally,

zebrin stripes show upbound and downbound properties depending

on CF connectivity, PC discharge regulation and PF-PC long-term

depression (see below).

Thus, we have an intricate mesoscopic organization that

overlaps with a broad mapping of anatomical subdivisions to bodily

and mental functions. This brings about two main consequences.

First, speciﬁc behavioral functions impinge on complex anatomic-

functional maps made of many microcomplexes and patches

rather than on simply maps residing in uniform cerebellar

regions and diﬀerentiated by their input-output connectivity.

Secondly, mapping of bodily and mental functions involves

multiple cerebellar regions, mostly due to the distribution of MF

terminal branches and PFs. These considerations are challenging

the historical view that the cerebellum is made of multiple

parallel non-communicating processing units, leaving space to

what we may call “multimodal fusion”. As a consequence, the

focus moves from the deﬁnition of cerebellar functions on

a pure anatomical basis to that of the cerebellar algorithm

that operates with diﬀerent inputs and outputs (D‘Angelo

and Casali, 2013). This algorithm, in turn, might be tuned

depending on the local properties of the microcomplexes that are

involved.

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FIGURE 1

The rodent cerebellum includes the midline vermis and the lateral hemispheres. The left side shows the anatomical subdivisions and the striped

pattern deriving from Aldoc (zebrin II) expression levels in PC (increasing expression levels from light to dark brown). In the vermis, subdivisions in

the anterior-posterior orientation are delimited by circumvolutions and ﬁssures and are further segmented into 10 lobules: the anterior cerebellum

(lobules I – V), the central cerebellum (lobules VI – VII), the posterior cerebellum (VIII-IXb), nodular cerebellum (lobule IXc-X), ﬂocculus (FI) and

paraﬂocculus (PFI). Lobules VI-VIII expand into large hemispheric lobules including crus I and crus II. The right side shows the cerebellar

olivo-cortico-nuclear modules deﬁned by IO-CF-PC-CN closed-loop connectivity, identifying seven parallel longitudinal zones (A, B, C1, C2, C3,

D1, D2). C, caudal; Cop, copula pyramidis; Cr I, crus I of ansiform lobule; Cr II, crus II of ansiform lobule; PFl, paraﬂocculus; Fl, ﬂocculus; L, lateral; Lt,

left; Par, paramedian lobule; R, rostral; Rt, right; Sim, simple lobule. Modiﬁed from Zhou et al. (2014) and Voogd (2014).

Function

Independent from the nature of inputs, being them motor,

sensory, cognitive, or emotional, the cerebellar algorithm learns to

predict the precise timing of correlated events (D‘Angelo and Casali,

2013).This algorithm establishes a causal relationship between

aﬀerent signals in sequence and can apply to diﬀerent contexts,

e.g., correlating elementary components of movement in motor

coordination, sensory stimuli in eye-blink classical conditioning

or in fear conditioning, or abstract items in logical or emotional

operations. In the realm of voluntary functions, the cerebellum

operates in a similar way by comparing intention (conveyed

through descending cortico-cerebellar pathways) with execution

(conveyed by aﬀerent sensory ﬁbers) to predict possible errors.

This de facto anticipates and corrects execution errors, generating

the precise predictive motor control typical of vertebrates.

These observations, combined with insightful theories, have been

extended to explain ﬂuid reasoning and mental coordination and

are arguably important also for emotional control.

Dynamics and mechanisms

On the mechanistic side, the main issue is how the cerebellar

units process input signals through their internal algorithm. In

other words, how do cerebellar neurons and synapses implement

such algorithm. This issue has been addressed by several

physiological and computational works, revealing a complex set

of neuronal and synaptic properties that can generate the input-

output transformation and plastic changes required to process

the input signals (for review see De Schepper et al., 2022). The

circuit operates a complex spatio-temporal transformation of the

MF input and uses the CF input to drive plasticity. On top of this,

the circuit governs intrinsic oscillatory dynamics that allow the time

correlation of the processes involved (D’Angelo, 2011;D’Angelo

et al., 2011).

A uniﬁed framework for motor,

cognitive, and emotional processing?

The intricate neuronal circuitry of the cerebellum is thought to

encode internal models that reproduce the dynamic properties of

body parts allowing the brain to precisely control the movement

without the need for sensory feedback. It is thought that the

cerebellum might also encode internal models that reproduce the

essential properties of mental representations elaborated in the

cerebral cortex. This hypothesis suggests a possible mechanism by

which intuition and implicit thought might function and explains

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FIGURE 2

Three layers compose the cerebellar cortex: the molecular layer, Purkinje cell layer, and the granular layer. The principal inputs to the cerebellar

cortex are conveyed by mossy ﬁbers (MF) originating from different brain stem and spinal cord nuclei and climbing ﬁbers (CF) originating from the

inferior olive. MFs contact GrCs and GoCs in the granular layer. GrC axon ascends to the molecular layer and bifurcates originating the parallel ﬁbers

(PF). GrC axons contact Purkinje cells (PCs) and molecular layer interneurons (MLI). PCs provide the sole output of the cerebellar cortex and feature

a wide, ﬂat, highly branching dendritic tree and a single long axon projecting to the deep cerebellar nuclei (DCN), from where cerebellar efferent

projections are sent to other brain areas. The MLI are Stellate and basket cells (SC, BC) located in the molecular layer. The candelabrum cells (CC)

(Lainé and Axelrad, 1994;Osorno and Regehr, 2022) are present in the PC layer, while unipolar brush cells (UBC) and lugaro cells (LC) are reported in

the granular layer.

aspects of cognitive and emotional reasoning as well as symptoms

exhibited by psychiatric patients (Ito, 2008).

Recently it was argued that a similar circuit structure in

all cerebellar areas may carry out various operations using a

common computational scheme (D‘Angelo and Casali, 2013).

The authors attributed diﬀerent roles of the cerebellum to

the speciﬁc connectivity of the cerebellar modules with motor,

cognitive, and emotional functions at least partially segregated

into diﬀerent cerebro-cerebellar loops. This led to formulate

ameta-levels hypothesis: from cellular/molecular to network

mechanisms leading to generation of computational primitives,

thence to high-level cognitive/emotional processing, and ﬁnally

to the sphere of mental function and dysfunction. It has been

proposed that the intimate interplay between timing and learning

(reminiscent of the “timing and learning machine” capabilities long

attributed to the cerebellum) reverberates from cellular to circuit

mechanisms. Subsequently, integration within large-scale brain

loops could generate the disparate cognitive/emotional and mental

functions in which the cerebellum is implicated. The cerebellum

should therefore operate as a general-purpose co-processor, whose

eﬀects depend on the speciﬁc brain centers to which individual

modules are connected. Abnormal functioning in these loops

could eventually contribute to the pathogenesis of major brain

pathologies including not just ataxia but also dyslexia, autism,

schizophrenia, and depression. These functions and dysfunctions

are deeply involving cognitive and emotional processing.

A system view

In engineering, the cerebellum has been assimilated to a double

forward and inverse controller, capable of predicting system states

based on contextual information and previous memory (Ito, 1993,

2008).The fact that our understanding of cellular phenomena

is suﬃcient to explain the predictive capabilities of the system

is demonstrated by the ability of neuro-robots, embedding a

canonical cellular representation of the cerebellar circuit dynamics

and mechanisms, to reproduce a wide set of sensory-motor control

tasks (Casellato et al., 2015;Antonietti et al., 2016, 2018, 2022). This

same system approach may be applied to cognitive and emotional

processing, provided the controller, to which the cerebellar circuit is

connected, is appropriately designed and implemented. Eventually,

a precise representation of the neuroanatomical principles based on

cerebellar connectivity and subdivisions, may help understanding

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the computational principles and mechanisms underlying the

cerebellar involvement in emotion (D’Angelo et al., 2016;D’Angelo

and Jirsa, 2022).

Cellular and circuit variants in the

rodent cerebellum

Since the cerebellar computational algorithm is likely to be

tuned depending on the local properties of microcomplexes,

it is important to consider how circuit properties vary across

cerebellar regions. This section is based on rodent studies, in which

accurate investigations have addressed the molecular and cellular

mechanisms and the potential sources of heterogeneity underlying

functional specialization.

Anatomical and physiological studies have shown that the

midline cerebellar vermis is particularly involved in emotional

processing and that lesions and other interventions on this

cerebellar compartment aﬀect emotion-related behaviors (reviewed

in Apps et al., 2018). The cerebellar vermis receives olivo-

cerebellar (CF) input from the caudal medial accessory olive and

emits a cortico-nuclear output to the medial (fastigial) cerebellar

nucleus which, in turn, has widespread projections to the cingulate

cortex, prefrontal cortex, parietal cortex, amygdala, hypothalamus,

hippocampus, periaqueductal gray (PAG), and striatum (Voogd

and Ruigrok, 2004;Blatt et al., 2013;Rolls, 2019) (see also next

chapter).

Diﬀerent cerebellar vermal lobules are associated with diﬀerent

aspects of emotion (see Figure 1). From rostral to caudal: lobules

IV–VI with fear learning/memory and aﬀective state; lobule VI–

VII with orientation of gaze; lobule VIII with fear-induced freezing

behavior; and lobule IX and X with cardiorespiratory control (Apps

et al., 2018). Diﬀerent parts of the vermis could therefore regulate

and integrate the cognitive, motor, and autonomic aspects of fear-

related behavior (Clausi et al., 2017).

Here, neuron and microcircuit variants are considered in

turn and a summary of properties that may be more relevant to

emotional processing is reported in the section on “Conclusion”.

The cerebellar microcircuit and its

variants

The cerebellar circuit includes several cell types connected

according to the general circuit scheme shown in Figures 2,3A.

A recent modeling work summarized the general knowledge on

the cerebellar microcircuit generating a reference ground-truth that

binds together structure, function, and dynamics (De Schepper

et al., 2022). In the granular layer, each glomerulus hosts ∼50

excitatory and ∼50 inhibitory synapses on as many GrC dendrites,

plus ∼2 excitatory synapse on basolateral dendrites of as many

Golgi cells (GoCs). Each one of the 4 GrC dendrites receives

an excitatory and (in most cases) an inhibitory input from as

many diﬀerent MFs and GoCs, respectively. Each GoC receives

∼320 ascending axon (AA) synapses on basolateral dendrites

and ∼910 PF synapses on apical dendrites, and there are ∼7-

8 electrical synapses plus 160 GABAergic (using the Gamma-

aminobutyric acid neurotransmitter) synapses per GoC-GoC pair.

In the molecular layer, ∼25% of AAs contact the distal dendrites

of the overlaying PCs (each AA forming 2.4 synapses on average),

while PFs form 1 synapse per PC dendritic intersection. In

summary, each PC receives 12% of the whole GrC inputs from

AAs. Among the molecular layer interneurons (MLI), we consider

the Stellate cells (SCs) and basket cells (BCs) populations. There

are ∼25 SC-PC and BC-PC synapses altogether. Moreover, there

are ∼17˙

600 PF-MLI-PC synapses (∼2˙

600 PF-SC-PC and ∼15˙

000

PF-BC-PC synapses).

Beyond the canonical description of the cerebellar network

reported above (Figure 3A), there is growing evidence that the

cerebellar cortex is not homogenous in structure, physiology, or

gene expression (Nishiyama and Linden, 2004;Ito, 2006;Sillitoe

et al., 2009;Fujita et al., 2012;Martinez et al., 2013;Nedelescu and

Abdelhack, 2013;Chen et al., 2022) and these variations of regional

features (Figure 3) may inﬂuence information processing. Several

cell clusters have recently been reported based on transcriptomic

proﬁle: there are 9 PCs clusters overlapping with zebrin stripes,

3 GrC clusters showing antero-posterior segregation (anterior-

central zone, central-posterior zone, nodular zone), 2 GoC clusters,

and two clusters of MLI including both SCs and BCs (Kozareva

et al., 2021).

Purkinje cells

Two large groups of PCs were identiﬁed based on the

expression of Aldoc (aldolase C, the enzyme responsible for the

zebrin II pattern) (Figure 3). The Aldoc positive and negative PCs

deﬁne the “zebrin stripes”, consisting of longitudinal alternating

bands of positive (Z +) and negative (Z-) PCs (see Figure 1). This

distribution is typical of the anterior and posterior cerebellar zones

(comprising lobules I - V, and VIII - dorsal IX, respectively), while

the central and nodular zones (lobules VI - VII and ventral IX -

X) are uniformly characterized by Z + PCs. From the functional

point of view, Z- PCs are characterized by higher spontaneous ﬁring

compared to Z + PCs (Xiao et al., 2014;Zhou et al., 2014). More

recently, Z- PCs were shown to undergo simple spike suppression

(downbound) (Wu et al., 2019) and Z + PCs to undergo simple spike

facilitation (upbound) (De Zeeuw, 2021).

In addition to heterogeneity between stripes, PCs also display

functional variations along the transverse axis. PCs in the anterior

lobes have more pronounced adaption of tonic ﬁring compared

to those in the nodular zone (Kim et al., 2013), where PCs show

lower ﬁring rates and reduced excitability (Witter and De Zeeuw,

2015). These diﬀerences likely make PCs in the nodular lobe more

suitable to integrate vestibular inputs, which are slower compared

to the faster and stronger inputs arriving in the anterior zone. An

anteroposterior gradient has been also reported for the regularity of

ﬁring (increasing toward the nodular zones) (Witter and De Zeeuw,

2015). The diﬀerence in PCs ﬁring along the transverse axis may

simply match the diﬀerent distribution pattern of zebrin, with more

zebrin negative PCs in the anterior cerebellum, and more positive

PCs in the posterior and nodular cerebellum.

PCs regional diversity also impacts the microcircuit

connectivity. In addition to the general scheme, in which

granule cells activate PCs and DCN cells in sequence, recent

evidence showed that PCs axon collaterals in the granular layer

form functionally active contacts onto GrCs (Guo et al., 2016) and

Unipolar Brushed Cells (UBCs) (Guo et al., 2021) that are less

represented in the anterior cerebellum (Figures 3B, C). Moreover,

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FIGURE 3

Cerebellar microcircuit variants. (A) Canonical circuit of the cerebellum. The primary input of mossy ﬁbers (MF) project to deep cerebellar nuclei

(DCN), granule cells (GrC), and Golgi cells (GoC). GoC inhibits GrC in a feedforward and feedback loop. Besides GoC, GrC axons activates Purkinje

cell (PC) and molecular layer interneurons (MLI). PCs provide the sole output of the cerebellar cortex and inhibit the cerebellar nuclei. (B) A ﬁrst

variant of the canonical circuit is represented by PCs axon collaterals projecting to GrC in the granular layer. (C) A second variant is represented by

unipolar brush cells (UBC), that are contacted by MF and excite GrC. UBC receive inhibition through PC axon collaterals. (D) A third variant shows

the nucleo-cortical projections: DCN axon collaterals contact GrCs with excitatory connections and inhibit GoCs, in the granular layer.

these collaterals show more complex morphology and more

numerous contacts in the granular layer starting from lobule VI

toward lobule X (Guo et al., 2016). This connection is characterized

by a low probability of GABA release, suggesting that a robust

activation of PCs is needed to activate this additional source of

feedback inhibition to GrCs. These results show that, in lobules

VI-X, PCs contribute to both phasic and tonic inhibition in the

granular layer, therefore aﬀecting the spatiotemporal dynamics of

GrCs ﬁring (D‘Angelo et al., 2013;Mapelli et al., 2014).

Unipolar brush cells

The canonical cerebellar microcircuit also diﬀers based on the

regional distribution of UBCs (Figure 3C). UBCs are excitatory

neurons involved in the MF-GrC pathway. These neurons are

located in the granular layer, with highest density in the medial

cerebellum, particularly lobules I, IX, X, and the lowest density

in the lobules IV-VI in mammals (Takács et al., 1999;Mugnaini

et al., 2011). UBCs receive a robust synaptic input by a single

MF and contact several GrCs, and are therefore called “intrinsic

mossy ﬁbers” (Nunzi and Mugnaini, 2000). Their response

patterns amplify the vestibular inputs, signiﬁcantly modifying the

transmission pathway from MFs to PCs (Nunzi and Mugnaini,

2000;Locatelli et al., 2013;Balmer and Trussell, 2019).

Granule cells

GrCs functions and connectivity are heterogeneously

distributed in diﬀerent subregions of the cerebellum. First, GrCs

receive excitatory inputs from UBCs, where these neurons are

enriched (mainly in the nodular zone). Second, PCs axon collaterals

contribute to GrC inhibition with a regional gradient increasing

toward posterior and nodular zones. It has also been reported

an antero-posterior gradient for GrCs eﬃciency in responding

to burst vs. tonic inputs, based on the diﬀerential expression of

the CaV3-Kv4 complex (Heath et al., 2014;Witter and De Zeeuw,

2015). This ﬁnding probably matches the diﬀerences in the inputs

(with slower vestibular inputs arriving at the posterior and nodular

zones). Moreover, GrCs receive excitation from nucleo-cortical

projections in several lobules, with a sagittal arrangement (Houck

and Person, 2014), which generates an internal ampliﬁcation loop

(Gao et al., 2016).

Golgi cells (and molecular layer interneurons)

Cerebellar inhibitory interneurons also show some speciﬁc

spatial arrangement, in particular concerning GoCs. GoC

dendrites are topographically organized within striped boundaries

determined by the zebrin II expression patterns in PCs. There

is no evidence in literature that diﬀerent GoC subtypes are

present in positive and negative stripes, though two clusters have

been identiﬁed based on the transcriptomic proﬁle (Kozareva

et al., 2021). Moreover, a recent study highlighted the functional

heterogeneity of GoCs depending on the expression of the human

glycine neuronal transporter type 2 (GlyT2), aﬀecting the inhibitory

control over GrCs (Dumontier et al., 2023). Additionally, GoCs

receive inhibition from nucleo-cortical projections in several

lobules (Ankri et al., 2015), decreasing GoC inhibition over GrCs.

MLIs are involved in complex inhibitory loops that shape PC

responses (Brown et al., 2019;Prestori et al., 2019). To the best of

our knowledge, a diﬀerential spatial distribution of MLIs subtypes

in diﬀerent zones or lobules has not been reported, yet.

Deep cerebellar nuclei cells

The cellular heterogeneity in the DCN is less studied, compared

to the cerebellar cortex. Nevertheless, DCN can be subdivided

based on the connectivity pattern in caudo-ventral and rostro-

dorsal axes, based on innervation from zebrin II positive and

negative PC, respectively [for a comprehensive review on this

subject see Kebschull et al. (2023)]. Diﬀerent functional nucleo-

cortical projections have also been identiﬁed in several lobules,

arranged in a sagittal organization mainly matching the territories

of PCs sending inputs to those DCN regions (Houck and Person,

2014). Therefore, nucleo-cortical projections (Figure 3D) match

the cortico-nuclear ones, originating closed loops, but the amount

of reciprocal connections can diﬀer in diﬀerent regions (Houck

and Person, 2014). Excitatory and inhibitory projections from DCN

aﬀect GrCs and GoCs activity, respectively (Ankri et al., 2015;

Gao et al., 2016). Anatomical tracing studies in rats reported that

certain regions of the cerebellar cortex seem to lack nucleo-cortical

projections, such as the lateral vermis (longitudinal zone B, see

Figure 1), the lateral paravermis (C3 zone), and some patches of

the paravermis C1 zone and the hemisphere (D zone) (Buisseret-

Delmas and Angaut, 1988, 1989). As a result, each lobule shows

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diﬀerent patterns of connectivity. For example, in cats, Lobule V

has been reported to lack nucleo-cortical projections in the C1 zone

(medial paravermis) (Trott et al., 1998a) and be enriched in them

in the C2 zone (lateral paravermis) (Trott et al., 1998b). Inside

zones, the distribution can be uneven, too. For example, the C2

zone of the paraﬂocculus lacks nucleo-cortical projections (Trott

et al., 1998b). Further anatomical and functional characterization of

these projections is needed to clarify the speciﬁc impact on regional

processing.

Structural variants

In addition to diﬀerences in local microcircuit connectivity,

there are also diﬀerences in the density and distribution of the

diﬀerent cell types as well as in the thickness of cerebellar regions.

Cell types and density

Geographical diﬀerences in PC pertain to neuronal locations,

packing density and diameter, as well as axon and dendritic

arbor morphology. In rats, there are fewer PCs at the base of

each cerebellar folium than at the apex (Braitenberg and Atwood,

1958;Eccles, 1967;Armstrong and Schild, 1970), and the packing

density of PCs in the anterior lobe is greater than that of the

posterior lobe (Armstrong and Schild, 1970). For example, Lobule

X is known to have a greater PCs density than other lobules,

such as lobules II and VI (Keller et al., 2018). There are also

signiﬁcant regional diﬀerences in PC size, with larger PC diameter

and organelle volume in phylogenetically older cerebellar regions

(such as the vermis) (Parma, 1969;Müller and Heinsen, 1984).

Consistent changes in PC axons diameter between white matter

compartments and dendritic arbor morphology at the base of a

folium compared to the apex have been described (Voogd, 2011;

Nedelescu and Abdelhack, 2013;Nedelescu et al., 2018). The

biophysical parameters and energy consumption of individual PC

in various cortical regions could be drastically altered by these

variations. Similarly, GrCs exhibit regional variations in packing

density, higher in the apex than in the base of a folium, and

bigger cell size in the vermis than in the hemispheres (Friede, 1955;

Cerminara et al., 2015). The packing density of GoCs in a variety of

mammals, including humans, is lower in the hemispheres (except

for the ﬂocculus) than in the vermis, where the densities are highest

in the ﬂocculus and lobules IX (Lange, 1974;Palay and Chan-Palay,

1982). Moreover, GoCs in the hemisphere are smaller than those in

the vermis (Geurts et al., 2001, 2003).

Cortical thickness

On average, the granular layer is 73% thicker than the molecular

layer (Zheng et al., 2022), whereas the Purkinje cell layer is

single cell thick. The diﬀerence in layer thickness constrains the

area occupied by neurons on the parasagittal surface, e.g., size

and width of PCs dendritic trees. Clearly, synapse development

and maturation as well as plasticity impact the branching and

elongation of dendrites (Takeo et al., 2021). Diﬀerence in layer

thickness will also impact the number of neurons, as in the granular

layer.

Granular layer thickness changes from the anterior to posterior

cerebellum, being much larger at lobules VI-VIII than lobules I-V

and smaller in lobules IX -X than in the ﬂocculus (Zheng et al.,

2022). In the depth of the sulci, all three layers are thicker than at

the crowns of the gyri. Globally, the lateral-posterior-inferior area

has a thicker granular layer than the medial-superior region.

Unique PCs dendritic tree shapes in the sulcus result in

more arbor ﬁeld overlap between nearby PCs than at the apex

(Nedelescu and Abdelhack, 2013) and cause quantitative variations

in dendritic branching patterns between lobules V and IX. Two

physically diﬀerent PC subtypes have been identiﬁed. Type 1

PCs adhered to the conventional concept of a PC morphology,

with a single primary dendrite and a rather uniform branching

density across the molecular layer’s thickness. On the other hand,

Type 2 PCs have two major dendrites and little branching in the

bottom portion. PCs in the anterior lobe (lobule V) of adult mice

exhibited a preponderance of type 1 dendritic arbors, while the

posterior lobe (lobule IX) contained a bigger proportion of type 2

isoforms. Overlapping in adjacent PC arbors varies across lobules,

with more overlap in lobule V than in lobule IX, and arbors

in lobule V using surrounding space more eﬃciently than those

in lobule IX. Those distinctions in neurons may throw out the

potential question concerning the aﬀerents system diﬀerentiation.

For example, lobules IV and V are the key area receiving signiﬁcant

MF projections from the lateral reticular nucleus, while lobules VI-

VIII and IX get a less dense distribution of this input (Wu et al.,

1999).

Atlas data for microcircuit and structural

variations

The cerebellum holds remarkable variations in cell

morphology, density, volume, and cellular properties. These

variations are further enriched considering microcircuit

connectivity of diﬀerent cerebellar regions, that go by lobules,

stripes, modules and eventually identify speciﬁc microzones and

microcomplexes. Using atlases introduce a new method to combine

evidence collected through the diﬀerent studies. For example, the

extensive datasets from the Allen Brain Institute (ISH Data.,

2023: Allen Brain Atlas: Mouse Brain.) and Blue Brain Cell Atlas

(Rodarie et al., 2022) provides exhaustive information on cell types

and densities that might be used for detailed model reconstructions

(Figure 4). This information must now be crossed with that on

cerebellar regional connectivity and behavioral localization of

functions addressing those regions and pathways that are critical

for emotional processing.

Cerebellar circuits in rodent emotional

behavior

While the cerebellum has canonically been implicated in motor

control and motor learning, which it exerts through a set of well-

studied circuits and pathways (Figure 5A), neuroimaging and

clinical data recently identiﬁed the cerebellum as a key region

among the emotion and cognitive relevant structures (Strick et al.,

2009;Apps and Strata, 2015;Strata, 2015;Adamaszek et al.,

2017). Anatomical studies in rodents have highlighted indirect

connections from the DCN, the sole output of the cerebellum,

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FIGURE 4

Neuron density and thickness in the mouse cerebellum based on the Blue Brain Cell Atlas. (A) Estimations of the thickness of the molecular layer in

mm for each subregion of the cerebellar cortex, using the algorithm proposed by Rodarie et al. (2022). (B) Estimations of the density of excitatory

and inhibitory neurons in the molecular and granular layer for each region of the cerebellar cortex. The data are based on the publicity available

Blue Brain Cell Atlas (2018) website.

to various brain structures involved in emotional (Figure 5B)

and cognitive processing (Figure 5C). For example, monosynaptic

projections from the DCN to the ventral tegmental area (VTA),

a structure included in the dopaminergic reward system, have

been described (Carta et al., 2019). Cerebellar output to the VTA

mainly originates from the dentate nucleus and, to a lesser extent,

from the interpositus nucleus (Baek et al., 2022). The optogenetic

activation of the cerebellum-VTA pathway is rewarding and

can condition mice behavior during place preference and social

interaction tests (Carta et al., 2019;Baek et al., 2022). Moreover, the

cerebellum and hypothalamus are highly interconnected through

direct and indirect routes. The reciprocal cerebellar-hypothalamic

pathway has been suggested to provide circuits through which the

cerebellum can inﬂuence the autonomic hypothalamic processes

activated during emotional states (Dietrichs and Haines, 1989;

Gritti, 2013). In fact, the cerebellum, via its connection with

the hypothalamus, is involved in controlling vasomotor reﬂexes

and blood pressure, carotid sinus reﬂexes, and the somatic

and autonomic manifestations of shame rage, pupil nictitating

membrane, respiration, gastrointestinal functions (Zhu et al., 2011).

Although a strong functional connectivity between the cerebellum

and a key aﬀective center, the amygdala, have been observed

(Supple et al., 1987;Tovote et al., 2015;LeDoux, 2000), the

anatomical substrate for this connectivity is still unclear. In fact, no

direct connections between the cerebellum and the amygdala exist.

Nevertheless, a putative di-synaptic pathway between the DCN

and the basolateral amygdala (BLA) through the thalamus (mainly

medial dorsal nucleus, centro-median, and parafascicular) has been

described (Gornati et al., 2018). Therefore, the cerebellar output

might inﬂuence the BLA, known to process aﬀect-relevant salience

and valence information (Jung et al., 2022).

Studying cerebellar role in fear using

animal models

Rodent behavioral studies have been extensively used to

investigate neurobiological aspects of emotions (see Box

“Emotional tasks in rodents”). However, ﬁnding valid and

objective measures of animal emotions can be challenging. The

most widely used emotional behavioral tests in rodents aim to

trigger a fear response (Fi. In fact, fear tests elicit a clear behavioral

readout and are highly reproducible. In fear conditioning, rodents

show a robust conditional response during fear memory retrieval

which is manifested in long periods of freezing behavior. Fear

conditioning is an associative learning task. For conditioning to

occur, pathways transmitting the conditioned stimulus (CS) and

unconditioned stimulus (US) have to converge in the brain. It is

widely believed that the amygdala is the site of CS-US convergence

(LeDoux et al., 1988;Koutsikou et al., 2014). Auditory (LeDoux,

1990;LeDoux et al., 1990;Mascagni et al., 1993;Romanski

and LeDoux, 1993) and other sensory inputs representing the

information about the CS terminate mainly in the lateral nucleus

of the amygdala (LA). LA, in turns, project to the central amygdala

(CeA) which controls the expression of fear response through

its projections to the brainstem. Fear conditioning to auditory

CS is thought to be mediated by these pathways. In contextual

fear conditioning, CS is represented by both the auditory and

the context cue. Information on the contextual stimulus involves

communication between the hippocampus and the basal and

accessory basal nuclei of the amygdala (LeDoux, 1992, n.d.).

These nuclei project to the CeA which controls the expression

of the response. Somatosensory information concerning the US,

including the nociceptive stimuli generated by the footshock

(commonly used in fear conditioning tests), are transmitted from

cortical and thalamic areas to the LA (Turner and Zimmer, 1984;

McDonald, 1998), mirroring the CS pathway. Moreover, CeA

receives nociceptive inputs from the parabrachial area (Bernard

and Besson, 1990) and directly from the spinal cord (Burstein

and Potrebic, 1993). CeA ultimately projects to diﬀerent brain

areas to drive the expression of fear response. Particularly, CeA

modulates the activity of the bed nucleus of the stria terminalis

on the release of the pituitary-adrenal stress hormone (LeDoux

et al., 1988;Herman et al., 2005;Pruessner et al., 2010), and the

lateral hypothalamus on the control of blood pressure. In addition,

CeA activates the ventrolateral periaqueductal gray (PAG) and

elicit freezing response. The PAG has long been implicated

as the organizer of behavioral components of the response to

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FIGURE 5

Efferent pathways from the cerebellum. Left: Schematic diagrams of historically studied efferent pathways related to motor functions (A), and of

direct or indirect efferent pathways that are considered to be associated with cognitive (B) and emotional (C) functions. Middle: target brain regions

from each DCN. Right: summary of broad functions of the pathways illustrated on the left. ALM, anterior lateral motor cortex; SMC, sensorimotor

cortex; SC, superior colliculus; Th, thalamus; RN, red nucleus; VN, vestibula nucleus; IO, inferior olive; RF, reticular formation; SpC spinal cord;

mPFC, medial prefrontal cortex; VTA, ventral tegmental area; PPN, peduncolopontine tegmental nucleus; RTN, reticulotegmental nuclei; DRN,

dorsal Raphe nucleus; HY, hypothalamus; PB, parabrachial nucleus; CeA, central amygdala; BLA, basolateral amygdala; PAG, periacqueductal gray.

Modiﬁed from Kang et al. (2021).

threat. It exerts direct control of freezing via glutamatergic

projections to premotor neurons targets in the magnocellular

nucleus of the medulla. Freezing behavior can be elicited from

the ventrolateral periaqueductal gray (vlPAG) in decerebrate

animals, therefore descending projections are necessary and

suﬃcient to elicit such behavioral response (Keay and Bandler,

2001). The cerebellum may also participate in the freezing

response. Indeed, anatomical evidence of projections from the

PAG to the cerebellar cortex have been reported (Dietrichs, 1983),

as well as direct projections from the cerebellar nuclei to the

vlPAG.

Based on early clinical and anatomical evidence, initial works

were conducted on the vermis. Using inactivation procedures,

the interpositus nucleus appeared to be involved in memory

formation of the freezing response to acoustic CS, whereas the

vermis appeared to be involved in the memorization of the freezing

response both to acoustic CS and in contextual tests (Sacchetti

et al., 2002). Furthermore, fear conditioning elicited long-term

potentiation at PF-PC synapses, in particular at cerebellar lobules

V and VI but not in IX and X. Interestingly, fear conditioning did

not modify CF synapses onto PC (Sacchetti et al., 2004).

Moreover, in vivo ﬁeld potential mapping techniques in

anesthetized rats revealed potent physiological connections

between vlPAG and cerebellar vermal lobule VIII (Koutsikou

et al., 2014). In fact, electrical stimulation of vlPAG on one

side of the brain evoked ﬁeld potentials on the cerebellar

cortical surface. Responses were largest in vermis lobule VIII,

predominantly localized laterally in the VIII, both sides. This

response is thought to be mediated by CFs activation since

barbituric anesthesia, used in the study, likely weakened MFs

activity. When lobule VIII was inactivated right after the animal

conditioning to CS-US pairings, fear memory retrieval showed a

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reduction in duration of freezing behavior to the CS tone only.

It has been hypothesized that the inactivation of lobule VIII

during the consolidation of the fear memory resulted in the

inability to retrieve the associative memory. However, a reduction

in freezing duration also occurred when CS-only memory

retrieval test was replaced with an innate fear test (predator

odor).

Cerebellum in autonomic response to

fear

Response to fear also includes body reactions as heart

rate and respiratory rate changes, vasomotor activity/blood ﬂow

modiﬁcation, and consequently sweating, trembling muscles,

increased body temperature (Nisimaru, 2004). The main pathway

known to mediate these eﬀects travels from the amygdala to the

hypothalamus which in turns activates the pituitary gland to secrete

hormones in the bloodstream and the autonomic nervous system.

In order to acknowledge the complex pattern of body reactions

to fear, Signoret-Genest and colleagues recently introduced the

concept of cardio-behavioral defensive state (Signoret-Genest et al.,

2023). From this perspective, freezing is not considered an isolated,

independent response to fear, rather the behavioral component

of a complex response pattern to fear. Such complex response

comprises rapid microstates associated with speciﬁc behavior and

heart rate dynamics, both aﬀected by long-lasting macrostates

reﬂecting context-dependent threat levels. If the cerebellum has a

role in freezing, which is part of such cardio-behavioral response,

then it might have a role in the other bodily responses to

fear. Indeed, the cerebellum has been proven able to inﬂuence

autonomic functions. For example, the electrical stimulation of the

vermis lobule IX causes changes in blood pressure and heart rate,

together with inhibition of the baroreceptor reﬂex (Bradley et al.,

1991).

On this matter, the direct and indirect projections from the

cerebellum to the hypothalamus, spinal visceral nuclei, dorsal

motor vagal nucleus, nucleus tracti solitari, parabrachial nucleus,

raphe nucleus, ambiguous nucleus, are expected to take part (Zhu

et al., 2011).

Furthermore, recent works have highlighted a strong functional

connectivity between the cerebellum and a region of the ventro-

lateral brainstem known as the pre-Botzinger complex (Lu et al.,

2013). The pre-Botzinger complex is considered the central pattern

generator of the respiratory rhythm (Smith et al., 1991) and

possibly the site of integration and coordination of diﬀerent

orofacial behaviors in mice, such as whisking, licking and

breathing (Lu et al., 2013;Romano et al., 2020). Moreover,

pre-Botzinger complex inhibitory neurons modulate sympathetic

vasomotor neuron activity, generating heart rate and blood

pressure oscillations in phase with respiration (Menuet et al., 2020).

Although there are no direct projections from the cerebellum

to the pre-Botzinger complex (Teune et al., 2000), cerebellar

nuclei project to the gigantocellular reticular formation and to

the parabrachial complex, which are both downstream of the pre-

Botzinger complex. In turns, these areas project to motor neurons

in the spinal cord to drive orofacial movements (Dobbins and

Feldman, 1994).

Anatomical and functional mapping

in humans

Thanks to recent developments in neuroimaging techniques,

direct studies on the brain of living humans, with or without

pathology, have become possible using minimally invasive tools.

These techniques can be used to study the structure of the brain,

e.g., Magnetic Resonance Imaging (MRI) and Computed Axial

Tomography, or its function, e.g., Functional Magnetic Resonance

Imaging (fMRI), Single Photon Emission Computed Tomography,

Positron Emission Tomography, Magnetoencephalography,

Electroencephalography and Long Latency Evoked Potentials

(Aine, 1995).

Functional neuroimaging studies provide a dynamic view of

brain activity. These studies expose the subject to a given stimulus

or context, like a cognitive task, to observe its behavior and record

the underlying brain activity. Among all, fMRI (Ogawa et al.,

1992) has become the most widely used functional neuroimaging

technique. fMRI not only allowed to quantify task-dependent

activations of brain regions by detecting changes in blood ﬂow, but

it also boosted the study of brain networks and connectome. In

particular, resting-state fMRI allows the identiﬁcation of functional

connectivity networks between speciﬁc areas (Fox and Raichle,

2007;Van Dijk et al., 2010). fMRI is also applicable to non-human

species (Gorges et al., 2017) and its use is rapidly extending to

rodent research [see for example (Pan et al., 2015, 2018;Grandjean

et al., 2020;Pradier et al., 2021)]. Nonetheless, fMRI has limitations

and its results are sometimes misinterpreted (Maestú et al., 2003;

Logothetis, 2008;Elliott et al., 2020).

Since emotion involves the integration of cognitive,

somatomotor, and autonomic functions, we will exploit the

privileged point of view oﬀered by human investigations to address

the concept of segregation and distribution of these same functions

across regions of the cerebellum.

Neuroimaging studies in human

cerebellum

A growing literature on functional neuroimaging (Stoodley

and Schmahmann, 2009;Guell and Schmahmann, 2020) evaluates

structure-function relationships in motor and non-motor areas

of the human cerebellum, together with clinical ﬁndings. These

studies highlight the involvement of diﬀerent circuits depending

on the task being performed. The imaging studies in humans

were used to reconstruct a functional topographic map of the

cerebellum. The results of these studies can be interpreted

according to Larsell’s nomenclature (Schmahmann et al., 1999),

that divides the cerebellum along the anterior-posterior axis

in lobules denoted by roman numerals from I to X (optional

labels, H and v, are used to denote the hemispheres and

the vermal region). In addition, the two largest hemispheric

lobules are separated in two parts, namely lobule VII (into

VIIA comprising crus I and crus II, and VIIB), and lobule VIII

(into VIIIA and VIIIB), according to the intrabiventer ﬁssures

(Figure 7A). It should be noted that, in human studies, the gross

subdivisions of the cerebellum are anterior and posterior, this

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BOX 1

Emotional tasks in rodents

Fear conditioning (FC) is a type of associative learning, in which rodents learn to associate a neutral stimulus (conditioned stimulus, CS; more often a tone but

sometimes a visual stimulus) with an aversive stimulus (unconditioned stimulus, US; usually a mild electrical foot shock), and show a consistent behavioral response

(conditioned response) (Figure 6A). The conditioned response is the freezing behavior, deﬁned as the complete absence of movements except those related to breathing

for at least 2 seconds. The FC protocol consists ﬁrst of an acquisition phase, often performed on day 1, where the animal is presented with a set of CS-US pairings and.

learns to fear both the tone and the training context. FC is learned rapidly, and after one conditioning session a very stable long-lasting behavioral change is produced

(consolidation). The next day, the amount of freezing during the presentation of CS alone (the tone) is used as a behavioral readout of memory retrieval. Depending on

the goal of the study, the retrieval phase can be performed in the same training environment as the conditioning, with or without the auditory cues (context FC, for

hippocampal-dependent learning), or in a diﬀerent environment, to rule out the contribution of the context in fear memory retrieval (amygdala-dependent learning).

Moreover, consolidation and stability of the fear memory can be assessed by performing the retrieval phase at some distance from the acquisition (delayed FC). Finally,

fear memory can undergo extinction when CS is presented alone for enough times (extinction phase).

The looming test (Figure 6B) is a non-associative fear test that triggers rodents innate behavioral response to approaching aerial predators. The innate fear response

does not require any previously acquired conditioning. A rodent is placed into an arena with a display monitor covering most of the ceiling, and an opaque/dark nest in

a corner of the arena representing a hiding spot. The looming stimulus consists of a black disc appearing on the display monitor above the animal and expanding,

mimicking the shadow of an approaching predator. This setting reliably and consistently triggers one of two defensive behaviors: escape toward the hiding spot or

freezing for a prolonged period. The sensory modality involved in triggering the behavioral response is vision. In particular, a subpopulation of neurons in the superior

colliculus seems to be necessary and suﬃcient to orchestrate these dimorphic defensive behaviors via two distinct pathways, eventually targeting a diﬀerent nucleus in

the amygdala (Wilensky et al., 2006;Tovote et al., 2015;Shang et al., 2018). Interestingly, pairing a neutral tone (CS) with a looming stimulus (US) fails to drive the

associative learning of the defensive response, in contrast with the robust learned response of the classical FC (Heinemans and Moita, 2022). However, comparison

between innate and learned fear studies suggest considerable overlap between circuit for innate and learned freezing and escape response, thus leaving the topic open for

further investigation.

In the predator odor test, rodents are exposed to a predator odor, a natural threat, and show an increase in fear behavior, particularly freezing and avoidance. Olfaction is

the most preserved sense throughout mammalian species and is the most prominent sensory modality in rodents. Moreover, early neuroanatomists and scientists

recognized a strong link between olfactory processing and emotions, including the olfactory areas in the emotional circuit (Papez, 1937). Indeed, later studies conﬁrmed

the role of the connections between olfactory structures and speciﬁc nuclei in the amygdala in predator odor-induced fear behavior, including freezing. Nevertheless, the

predator odor test has been progressively disregarded, possibly due to the issues in standardizing the odor stimulus, the high variability in the behavioral response, and

the low reproducibility of the test.

Anxiety tests

Elevated plus maze, Open ﬁeld test, Dark-light box test, Hole board test, Social novelty test, Novelty exploration, Marble burying test.

An array of behavioral tests has been developed to investigate anxiety-like behaviors in rodents, so called because they resemble anxiety behaviors in humans (although

in rodents they might represent something else entirely). In humans, anxiety and fear produce similar behavioral responses, including but not limited to increasing

vigilance, freezing and/or hypoactivity, elevated heart rate, and suppressed food consumption. As opposed to fear, anxiety is elicited by aversive stimuli or a sense of

threat which are diﬀuse, unpredictable, and of long duration. In rodents, anxiety-like behavior is assessed by measuring the response to a novel and potentially

threatening environment or object. Anxiety tests depend on locomotion and take advantage of the innate aversion rodents show for bright light and open areas. This is

the case for approach-avoidant paradigms such as Elevated plus maze (Figure 6C), Open ﬁeld test (Figure 6D), Dark-light box test (Figure 6E), and Hole board test

(Figure 6F). These tests assess the natural exploratory behavior of the rodents, which is supposed to be reduced or suppressed when the animals feel a threat. Other

widely used tests rely on the curiosity towards new objects (Figure 6G) or social novelty (Figure 6H), which is altered by stress and anxiety. More creative and less

used tests, such as the Marble burying test (Figure 6I), exploit rodents’ tendency to dig the bedding of their cages and hide objects when stressed.

Often, these tests are inconclusive and non-reproducible because of the high inter-animal and inter-session variability of the results. These inconsistencies are attributed

to the individual genetic variations (as genetic background and strains), testing environment (handling, testing rooms, equipment), protocols, and the rearing

environment of the rodents (housing environment).

latter includes superior and inferior parts and the ﬂocculo-nodular

lobe.

In general, in functional studies, the cerebellum is usually

divided into two representations, motor and non-motor (Stoodley

and Schmahmann, 2009;Buckner et al., 2011;Stoodley et al.,

2012;Guell et al., 2018b, 2019;Guell and Schmahmann, 2020).

Speciﬁcally, areas involved in motor representations are the

anterior lobe and parts of lobule VI and VIII, while the areas

involved in non-motor representations are lobules VI, crus I, crus

II, VIIB and IX with extensions to lobule X. Some authors, such

as King et al. (2019), promotes a functional parcellation to re-

deﬁne lobar boundaries as it better matches the experimental data.

Also, Boillat et al., 2020 highlighted that the activation patterns

observed in functional studies do not strictly adhere to lobar

boundaries, and suggested that the cerebellar cortex is a continuous

sheet.

Although extensive fMRI studies have been performed, a

composite uniﬁed picture of the cerebellar functional topography

is still lacking. Figure 7B represents the activation of the diﬀerent

cerebellar lobules in motor, cognitive, emotional, and social tasks.

The most signiﬁcant observation coming from our data metanalysis

is that, besides a preference for speciﬁc modalities, there is a

considerable overlap of functions in several cerebellar areas.

Motor cerebellum

As commonly used in literature, we distinguish motor tasks into

simple motor tasks,eye movements tasks, and complex motor tasks.

Studies on simple motor tasks (Riecker et al., 2005;Stoodley

and Schmahmann, 2009;Guell et al., 2018a;Boillat et al., 2020)

report activation of the anterior lobe and lobule VIII, conﬁrming

the electrophysiological studies of body maps (Snider and Eldred,

1951). More speciﬁcally:

1. Right/left leg and foot movements activate ipsilateral lobules II,

III, and IV with extensions to lobule I,

2. Orofacial movements activate medial regions of lobule VI,

3. Right/left hand movements activate ipsilateral lobules V, VIII,

4. Tongue movement activates ipsilateral lobule VI,

5. Articulation activates medial hemispheric lobules IV to VI and

VIII.

Studies on eye movements (Dieterich et al., 2000;Dimitrova

et al., 2002;Alvarez et al., 2010;King et al., 2019;Boillat et al., 2020)

report activation of the anterior lobe and lobule VIII, including

vermal areas and lobule VII. For example: optokinetic reﬂex, smooth

pursuit, vergence and saccades activate vermal lobules VI and VII

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FIGURE 6

Schematic representation of emotional tasks in rodents, as detailed in the Box. Fear conditioning (A). Looming test (B). Elevated plus maze (C). Open

ﬁeld test (D). Dark-light box test (E). Hole board test (F). Novel object test (G). Social novelty test (H). Marble burying test (I).

and extend into vermis lobules IV-V and adjacent medial regions

of crus I and crus II.

Studies on complex motor tasks (Debas et al., 2010;Ma et al.,

2010;Schlerf et al., 2010;Stoodley et al., 2012;Hardwick et al.,

2013;Ashida et al., 2019;King et al., 2019;Tzvi et al., 2020) report

activation of the anterior lobe, lobule VIII as well as lobule VII.

More speciﬁcally:

1. Motor learning activates bilateral anterior/medial cerebellum

extending into lobule VI,

2. Motor planning activates lobules VI, crus I and crus II,

3. Motor sequencing activates anterior regions extending into

lobule VI, VIIIA/B and crus II,

4. Motor adaptation activates anterior lobules IV-V, VIII

bilaterally,

5. Visuomotor adaptation activates lobules IV-V, VI, VIII, crus II,

6. Finger tapping activates ipsilateral anterior lobe (lobules IV-V)

and lobules VI, VIII,

7. Left and right button press activates ipsilateral anterior lobe

and lobule VIII,

8. Skilled movement activates lobules VI, crus I,

9. Execution (reported in both motor and cognitive studies)

activates lobes V, VI, VIII,

Clinical studies (Urban et al., 2003;Kumral et al., 2007;

Schmahmann et al., 2009;Stoodley and Schmahmann, 2009;

Maderwald et al., 2012;Bultmann et al., 2014;Stoodley et al., 2016)

support the previously reported experimental ﬁndings on motor

tasks. Aﬀections of these lobules or regions have been additionally

found to be related to neuromotor disorders. For example, patients

with poor ﬁnger tapping have lesions in the ipsilateral anterior

cerebellum, ataxia scores correlate with damage of the anterior lobe

(lobules II to V extending into lobule VI), and lesions responsible

for cerebellar dysarthria include lobules I, II, III.

Cognitive cerebellum

fMRI analyses in cognitive tasks (Fink et al., 2000;Stoodley and

Schmahmann, 2009;Stoodley et al., 2012;Balsters et al., 2014;E

et al., 2014;Guell et al., 2018a;Ashida et al., 2019;Casiraghi et al.,

2019;King et al., 2019) report vermal activation of lobules VI and

VII as well as hemispheric activation of these lobules with lobule

VIIIA and extensions to lobule V. More speciﬁcally:

1. Action/execution tasks (reported in both motor and cognitive

studies) activate lobules V, VI, VIII and regions of crus I

2. Action/observation tasks activate areas of lobule VI, crus I and

crus II,

3. Working memory activates hemispheric lobules VI, crus I, and

VIIB to VIIIA,

4. Visuospatial working memory and attention activate

hemispheric lobules VI, crus I,

5. Linguistic and reading tasks activate right lobules VI, VII, and

VIIIA,

6. Spatial (mental rotation) activate left-lateralized lobules VI,

VII to VIIIA, crus I and midline VIIA,

7. Spatial (navigation/orientation/mutual rotation) activate right

crus I, bilateral lobules VI, and VII.

Clinic evidence (Schmahmann and Sherman, 1998;Mariën

et al., 2014;Stoodley et al., 2016;Hoche et al., 2018) proves

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FIGURE 7

Contribution of the human cerebellum to motor, cognitive, emotional, social and complex task. (A) Classical nomenclature of the division of the

human cerebellum, from anterior lobe (I to V) to posterior lobe (Superior posterior: lobules VI to VIIB and Inferior posterior: lobules VIIIA to IX)

separated by intrabiventer ﬁssures (dark black line), and ﬂocculo-nodular lobe. (B) Association of the activation of the different cerebellar lobes

(in colors) to different functional tasks, based on the consulted bibliography. The tasks were divided into motor, cognitive, emotional, and social.

Complex experiences cannot be strictly classiﬁed into motor, cognitive, emotional, or social tasks. (C) Lobules involvement in functional tasks of

panel (B). The height of each bar corresponds to the number of tasks (out of the 17 listed) related to the activation of each lobule.

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FIGURE 8

Venn’s diagram based on the consulted bibliography of three functional domains (motor, cognitive, emotional/social). In this diagram, the emotional

and social tasks are joined within the same functional domain, and complex experiences are excluded. The Venn’s diagram shows the lobules

involved in motor, cognitive, and emotional/social tasks. Overlapping parts contain the lobules that are common to two or all regions. Notice that

lobules involved in the cognitive function are in common with both the motor and emotional/social function.

that cognitive impairments correlate with posterior lobe lesions

(lobules VI and VII, including crus I, crus II) in abstract

reasoning, working memory, planning, executive functioning,

spatial cognition, language, and cognitive regulation. Damage to

the left hemisphere is more related to spatial diﬃculties, while the

right cerebellar hemisphere to language problems.

Emotional cerebellum

Most studies (Kilts et al., 2003;Wildgruber et al., 2005;Stoodley

and Schmahmann, 2009;Stoodley et al., 2010, 2012;Moulton et al.,

2011;Baumann and Mattingley, 2012;Schraa-Tam et al., 2012;

Adamaszek et al., 2014, 2017;Nummenmaa et al., 2014;Lange et al.,

2015;An et al., 2018;Ernst et al., 2019;King et al., 2019;Thomasson

et al., 2019) agree that the regions involved in emotional processing

are in the posterior lobes, including the vermal area.

Vermal and paravermal regions are involved in fundamental or

primary emotions, namely:

1. Anger: vermal lobule IX and paravermal lobules VI, crus I and

crus II,

2. Sadness: vermal lobules, crus I, crus II and paravermal lobule

VI,

3. Disgust; vermal lobules V, VIIIA to IX and paravermal lobule

VI,

4. Fear: vermal lobule VIIB and paravermal lobes VI, crus I and

crus II,

5. Happiness: paravermal lobe VIIIB.

In the domain of Emotional recognition:

1. Seeing emotional vs neutral images activates posterior lobes,

including lobules VI, VII

2. Seeing pleasant images activates the right lobules VI and crus

3. Seeing unpleasant images activates bilateral lobules VI to

VIIIB, vermis of lobule IX, and left crus I

4. Processing angry facial expression activates the right lobule V

5. Identifying emotional intonations activates midline lobule VII

and lateral lobules VI and crus I

6. Listening to other’s spoken emotional narratives induces

activation in lobules VI and IX, right lobules VIIB, VIII, and

7. Processing of emotion-laden visual and auditory art activates

crus I and crus II is reported.

Emotion regulation activates vermal and hemispheric lobules

VI, crus I, VIIB, VIIIA/B and IX. Emotional memory activates crus

II and lobules VI, IX, X.

Finally, Fear learning activates the culmen, right and left

hemispheric lobule IV-V and left lobule VI, and right and left lobule

hemispheric lobule IX.

Social cerebellum

The study of the role of cerebellum in social behaviors (Van

Overwalle et al., 2014, 2020;Leggio and Olivito, 2018) has recently

gained attention. More precisely, on mentalizing task which

evaluate the ability to understand the mental state – of oneself or

others – that underlies overt behavior:

1. social mentalizing activates crus I, crus II, and lobule IX,

2. person mentalizing activates crus I, lobules IV and VI,

3. event mentalizing activates crus I,

4. abstract mentalizing activates crus I, lobules VI and IX.

Mirroring is the behavior in which one person

subconsciously imitates the gesture, speech pattern,

or attitude of another). This task activates crus I,

lobules VIIB and VI.

Clinical data (Levisohn et al., 2000;Schmahmann

et al., 2007;Tavano et al., 2007;Adamaszek et al., 2014)

report the relationship between damage to the posterior

vermis with extensions in the medial cerebellar regions

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with social, behavioral, aﬀective deﬁcits and emotional

disorders in patients with aﬀect dulling, anxiety, depression,

impulsivity, irritability, aggression and autism spectrum

disorder.

Complex experiences involving multiple

cerebellar areas

fMRI studies indicate that the cerebellum responds to other

complex experiences such as aversive and painful stimuli (Becerra

et al., 1999;Ploghaus et al., 1999;Dimitrova et al., 2003;Singer

et al., 2004;Jackson et al., 2005;Moulton et al., 2011;Borsook et al.,

2018), interoceptive stimuli (Parsons et al., 2000) as well as sexual

experiences (Holstege and Huynh, 2011;Wise et al., 2017).

Aversive stimuli (noxious heat, electricity, unpleasant images)

activate lobule VI, crus I, VIIB, while painful stimulation evokes

bilateral activation of lobules III, IV and VIII as well as the vermal

and hemispheric parts of lobule VI and crus I. Therefore, pain

experiences activate multiple areas: the anterior regions in relation

to the motor component as reﬂex, the posterior regions in relation

to the anticipation of pain or unpleasant situation, and the posterior

vermis for the painful experience itself including fear or startle

reactions. Pain empathy activates bilaterally lobule VI and crus I.

Interoceptive stimuli such as thirst activates vermal lobe III,

whereas during maximal thirst lobes X, VI and crus I are activated,

and the left lobe VI is strongly activated after drinking to satiety.

Man ejaculation evokes activation in lobule VIII, right vermal

lobule V, left vermal lobule VII, left lobule VI, and bilateral crus

I, moreover women orgasm elicited activity left lobule V and right

lobule III, while during the post-orgasm recovery activates vermal

and hemispheric lobule VI and lobule VII.

It would be wrong to limit an area of the cerebellum strictly to

either motor, cognitive, emotional or social tasks. However, lobules

task-dependent predominance can be delineated (Figures 7,8).

Lobule VI as an integrating region

Figure 7B shows how the lobule VI is involved in all the

reported tasks of our collected literature studies. This lobule

might represent an integrating or modulating node between motor,

cognitive, emotional, and social areas. This may be due to the

connection of this area with multiple cortical and brain stem areas.

It would be interesting to see whether a multimodal input implies

cells with larger dendrites to integrate more synaptic information.

Extra cerebellar connectivity

Studies on resting-state functional connectivity showed that

most of the cerebellar regions partake in 5 intrinsically-connected

circuits or networks (Habas et al., 2009;Brissenden et al., 2018):

1. the sensorimotor network (lobule V-VI and VIII),

2. the default-mode network (lobules VIIa and IX),

3. the right and left central executive network (lobule VIIa),

4. the dorsal attentional network (lobules VIIB and VIIIA)

5. the salience network (lobules VI and VII).

Furthermore, at least 2 functional macroscale circuits involving

the cerebellum have been identiﬁed (Leggio and Olivito, 2018):

asensorimotor zone (anterior cerebellum) functionally correlated

with premotor, motor, somatosensory, visual, and auditory cortical

regions, and a supramodal zone (posterior cerebellum) functionally

correlated with dorsolateral prefrontal and inferior posterior-

parietal regions.

Resting-state fMRI studies report connections of the

cerebellum with cerebral cortex (Krienen and Buckner, 2009;

O’Reilly et al., 2010), striatum (Stoodley and Schmahmann,

2009), basal ganglia (Bostan et al., 2010, 2013), antero-medial

and mediodorsal thalamus, and habenula (Milardi et al., 2016;

Habas and Manto, 2018). Moreover, tracts have been identiﬁed

between the medial part of posterior lobule of the cerebellum with

insula (Cauda et al., 2011;Baur et al., 2013), frontal operculum,

anterior cingulate cortex, medial prefrontal cortex, amygdala, and

hippocampus, as well as between the lateral part of the cerebellum

with the hypothalamus and anterior cingulate cortex (Sang et al.,

2012;Bostan et al., 2013;Murty et al., 2014;Habas, 2018).

Limitations of in vivo neuroimaging

studies of the cerebellum

Neuroimaging studies are aﬀected by limited spatial and

temporal resolution, although this has recently been increased by

technological improvements such as fMRI, electroencephalography

(Huster et al., 2012;Jorge et al., 2014;Croce et al., 2016) and

advanced analysis methods (Spencer and Goodfellow, 2022). The

ﬁne folded pattern of the layers of the cerebellar cortex makes it

even more complicated to deﬁne cerebellar connectivity in detail

in humans. Indeed, the surface of the cerebellar cortex is much

more tightly folded than that of the cerebral cortex. Thus, although

the visible surface of the cerebellum is several times smaller than

that of the neocortex, once unfolded it equals 78% of the total

surface area of the neocortex (Sereno et al., 2020). This, together

with the extended connectivity to associative cortical areas (Palesi

et al., 2015), suggests a prominent role for the cerebellum in the

evolution of distinctive human behaviors including cognition and

emotion.

Conclusions and outstanding

questions

Segregation, distribution, and

specialization

The evidence reviewed here reveals, as a fundamental aspect of

emotional processing in the cerebellum, the interplay of segregation

and distribution of functions in multiple cerebellar regions. The

corollary is the concept of specialization, that could adapt the

general circuit mechanisms of the cerebellum to the speciﬁc needs

of emotional control.

Segregation. Numerous studies (Stoodley et al., 2010, 2021;

Solstrand Dahlberg et al., 2020) have shown that lobules of the

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central cerebellum, in particular lobules VI, VII, and VIII, show

most of the fMRI-related activity during emotional tasks. Likewise,

the data summarized in Figure 7B indicate that several cerebellar

regions are engaged in multiple functions, as opposed to being

speciﬁc for a single function. It is plausible to ask whether and to

what extent the functional specialization of lobules VI, VII, and

VIII entails molecular, cellular, or local network specializations as

compared to other modules. The search for circuit specializations

in the cerebellum may start from central lobules, especially lobule

VI that presents the most extensive implications for emotional

processing (Figure 7C).

Distribution. Since the emotional response entails

sensorimotor, cognitive, and autonomic aspects, numerous

cerebellar lobules that generically operate motor or autonomic

control are indeed also contributing to emotion. As a corollary,

some lobules should be convergence sites for emotional, cognitive

and motor responses, as we have pointed out for lobule VI.

Specialization. The processing role of the cerebellum at system

level notwithstanding, the cerebellar network repeats itself in

the general motive but not in the details, suggesting that some

properties may diﬀerentiate microcircuit computation. Focusing

on central lobules, the following pattern emerges:

1. Lobules VI and VII are almost entirely Z + (or upbound),

PCs have lower ﬁring rate and show PF-PC long-term

potentiation. Lobule VIII has alternating Z + /Z- stripes.

2. Lobules VI to X show an increasing amount of PC axonal

collaterals contacting GrCs, UBCs, and GoCs.

3. Lobules VI to VIII have almost no UBCs.

4. Lobule VI to VIII have higher cortical thickness than other

lobules, and lobule VI has a higher PC density than most of

the other lobules.

5. There is an antero-posterior gradient in GrC and PC ﬁring

adaptation.

It is possible that these diﬀerent properties subtend the higher

need for multimodal integration of lobules VI-VIII bringing about

larger input-output ﬁber sets, diﬀerent management of recurrent

loops and local processing of signals on speciﬁc frequency bands,

diﬀerential processing of burst and tonic ﬁring depending on

the nature of the input, diﬀerent needs for plasticity generation

and memory storage. To what extent these properties impact on

microcircuit function and spatiotemporal dynamics is currently

unknown and its understanding will require accurate physiological

recordings and model simulations.

Implications for behavioral processing

Since the cerebellum is involved in learning the association of

time-correlated events and it is likely to do so in all its modules

(D‘Angelo and Casali, 2013), a core hypothesis is that the cerebellum

orchestrates the time domain for the suppression/initiation of fear

responses as much as it does in other conditioning tasks, e.g., in

eye-blink classical conditioning. In other words, the cerebellum

should perform in emotional control the same operation it

performs in motor control and coordinate the many components

of the emotional response including motor and visceral reactions.

Lobules VI to VIII are involved and studies in rodents support

their functional connection to the amygdala, hypothalamus, and

periaqueductal gray. Regions IX and X play a major role in

orchestrating the autonomic response. In humans, the pattern of

explorable emotions is larger than in rodents, but evidence still

supports a specialization of lobules VI to VIII. In humans, the

high resolution of fMRI techniques during tasks has reinforced

the concept of a remarkable distribution and overlapping of

areas involved in emotional control with those controlling motor,

cognitive, and social behaviors. In addition to coordinate the

components of the emotional response, the cerebellum may

likewise be involved in comparing the cortical commands with their

execution to predict possible errors and correct them. Again, the

cerebellum may play the same coordinating role in emotion as it

plays in sensorimotor control.

Author contributions

CCia, YL, and DO: writing – original draft preparation

and visualization. ED’A, LM, CCas, and DR: writing –

review and editing. ED’A, LM, and CCas: supervision. ED’A:

conceptualization. All authors contributed to the article and

approved the submitted version.

Funding

This work has received funding from the European Union’s

Horizon 2020 Research and Innovation Program under the Marie

Skłodowska-Curie grant agreement No. 956414. “Cerebellum and

emotional networks” and under the Flagship program No. 945539

“Human Brain Project, SGA3”. This work was supported by

#nextgenerationEU (NGEU) and funded by the Ministry of

University and Research (MUR), National Recovery and Resilience

Plan (NRRP), project MNESYS (PE0000006) – A Multiscale

integrated approach to the study of the nervous system in health

and disease (DN. 1553 11.10.2022). DR has received funding from

the Centro Ricerche Enrico Fermi.

Conﬂict of interest

The authors declare that the research was conducted in the

absence of any commercial or ﬁnancial relationships that could be

construed as a potential conﬂict of interest.

Publisher’s note

All claims expressed in this article are solely those of the

authors and do not necessarily represent those of their aﬃliated

organizations, or those of the publisher, the editors and the

reviewers. Any product that may be evaluated in this article, or

claim that may be made by its manufacturer, is not guaranteed or

endorsed by the publisher.

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Full-text available

Nov 2023

The cerebellum, traditionally associated with motor coordination and balance, also plays a crucial role in various aspects of higher-order function and dysfunction. Emerging research has shed light on the cerebellum's broader contributions to cognitive, emotional, and reward processes. The cerebellum's influence on autonomic function further highlights its significance in regulating motivational and emotional states. Perturbations in cerebellar development and function have been implicated in various neurodevelopmental disorders, including autism spectrum disorder and attention deficit hyperactivity disorder. An increasing appreciation for neuropsychiatric symptoms that arise from cerebellar dysfunction underscores the importance of elucidating the circuit mechanisms that underlie complex interactions between the cerebellum and other brain regions for a comprehensive understanding of complex behavior. By briefly discussing new advances in mapping cerebellar function in affective, cognitive, autonomic, and social processing and reviewing the role of the cerebellum in neuropathology beyond the motor domain, this Mini-Symposium review aims to provide a broad perspective of cerebellar intersections with the limbic brain in health and disease.

The cerebellar nuclei take center stage

Article

Jun 2024

Dana Cohen

Cerebellar control over inter-regional excitatory/inhibitory dynamics discriminates execution from observation of an action

Preprint

Full-text available

May 2024

The motor learning theory anticipates that cerebro-cerebellar loops perform sensorimotor prediction thereby regulating motor control. This operation has been identified during action execution (AE) and observation (AO) but the causal interaction between the cerebellum and cerebral cortex remained unclear. Here we used Dynamic Causal Modelling (DCM) to study functional MRI (fMRI) data obtained during a squeeze ball task in either the AE or AO conditions. In both cases, active regions included bilateral primary visual cortex (V1), left primary motor cortex (M1), left supplementary motor and premotor cortex (SMAPMC), left cingulate cortex (CC), left superior parietal lobule (SPL), and right cerebellum (CRBL). AE and AO networks showed the same fixed effective connectivity, with pathways between V1, CRBL, SMAPMC and CC wired in a closed loop. However, the cerebellar communication towards the cerebral cortex switched from excitatory in AE to inhibitory in AO. Moreover, in AE only, signal modulation was non-linear from SMAPMC to CRBL and within the CRBL self-connection, supporting the role of the CRBL in elaborating motor plans received from SMAPMC. Thus, the need for motor planning and the presence of a sensorimotor feedback in AE discriminate the modality of forward control operated by the CRBL on SMAPMC. While the underlying circuit mechanisms remain to be determined, these results reveal that the CRBL differentially controls the excitatory/inhibitory dynamics of inter-regional effective connectivity depending on its functional engagement, opening new prospective for the design of artificial sensorimotor controllers and for the investigation of neurological diseases.

Cerebellar contributions to fear-based emotional processing: relevance to understanding the neural circuits involved in autism

Article

Full-text available

Nov 2023

Cerebellar networks have traditionally been linked to sensorimotor control. However, a large body of evidence suggests that cerebellar functions extend to non-motor realms, such as fear-based emotional processing and that these functions are supported by interactions with a wide range of brain structures. Research related to the cerebellar contributions to emotional processing has focussed primarily on the use of well-constrained conditioning paradigms in both human and non-human subjects. From these studies, cerebellar circuits appear to be critically involved in both conditioned and unconditioned responses to threatening stimuli in addition to encoding and storage of fear memory. It has been hypothesised that the computational mechanism underlying this contribution may involve internal models, where errors between actual and expected outcomes are computed within the circuitry of the cerebellum. From a clinical perspective, cerebellar abnormalities have been consistently linked to neurodevelopmental disorders, including autism. Importantly, atypical adaptive behaviour and heightened anxiety are also common amongst autistic individuals. In this review, we provide an overview of the current anatomical, physiological and theoretical understanding of cerebellar contributions to fear-based emotional processing to foster further insights into the neural circuitry underlying emotional dysregulation observed in people with autism.

How the Inflamed Skull-Box Might Ruin the Cerebello-Cerebral Social Repertoire: Imaging, Testing, Rescuing with Currents

Article

Full-text available

Oct 2023
PSYCHIAT DANUB

Gottfried Treviranus

Nascent cerebellar neuropsychiatry is rewriting complex human relations. In daily practice this sheds light on subsets of therapy-resistant patients, who feel hampered by a lack of skills in predictively presensing the trajectories to where especially their interpersonal appropriations might end up. Humans affected by "dysmetric" social phobia often lead minimal lives, strongly dislike exposition, suffer fatiguability also from immune dysfunctions, and anhedonia. In social dysmetria especially on the cerebellar cortex`s both lateralmost Crus-II (Van Overwalle) seem damaged, the left Crus-I may add agentic failure (Guell). The cerebellum, and the basal ganglia together discipline the cortex through parallel closed circuits partly interconnected through the thalamus. "Patho-trajectories" of the cere-bellum in daily care emerge through also neurological ataxiology and MRI-imaging. A uniquely pontine axonal diffusive damage to an executive-control-loop as core of "p-factor", the prime broad risk factor, points to an immune-arterial hotspot at the root of cerebellar arterial supply, as supported by the pediatric mostly inflammatory somatic "d-factor". While localizations refer back to his "phrenology", F. G. Gall's contention, that skulls, plausibly via mycobacteria, were relevant, are rehabilitated by calvario-menigeal vessels, which added the skull to the new brain logistics. Mast cells are uniquely positioned to cause superficial and deep cortical pathologies, and, enclosed in its subtentorial posterior fossa, also the cerebellum is exposed to various, often intracellular, smoldering originally dental or ORL-infections exemplifying non-neural psychiatric etiologies.

HOW THE INFLAMED SKULL-BOX MIGHT RUIN THE CEREBELLO-CEREBRAL SOCIAL REPERTOIRE: IMAGING, TESTING, RESCUING WITH CURRENTS

Conference Paper

Full-text available

Aug 2023

Gottfried Treviranus

How the inflamed skull-box might ruin the cerebello-cerebral social repertoire: Imaging, testing, rescuing with currents.

Preprint

Sep 2023

Gottfried Treviranus

Nascent Harvardian Italo-Belgian cerebellar neuropsychiatry is rewriting complex human relations. In daily practice this sheds light on subsets of therapy-resistant patients, who feel hampered by a lack of skills in predictably pre-sensing the trajectories to where especially their interpersonal appropriations might end up. Humans affected by "dysmetric" social phobia often lead minimal lives, strongly dislike exposition, suffer fatigability also from immune dysfunctions, and anhedonia. In social dysmetria especially on the cerebellar cortex`s both lateral-most Crus-II (Van Overwalle) seem damaged, the left Crus-I may add agentic failure (Guell). The cerebellum, and the basal ganglia together discipline the cortex through parallel closed circuits partly interconnected through the thalamus. "Patho-trajectories" of the cere-bellum in daily care emerge through also neurological ataxiology and MRI-imaging. A uniquely pontine axonal diffusive damage to an executive-control-loop as core of "p-factor", the prime broad risk factor, points to an immune-arterial hotspot at the root of cerebellar arterial supply, as supported by the pediatric mostly inflammatory somatic "d-factor". While localizations refer back to his "phrenology", F. G. Gall's contention, that skulls, plausibly via mycobacteria, were relevant, are rehabilitated by calvario-meningeal vessels, which added the skull to the new brain logistics. Mast cells are uniquely positioned to cause superficial and deep cortical pathologies, and, enclosed in its subtentorial posterior fossa, also the cerebellum is exposed to various, often intracellular, smoldering originally dental or ORL-infections exemplifying non-neural psychiatric etiologies. - In memoriam Riccardo Delle Chiaie

Cerebellum Lecture: the Cerebellar Nuclei—Core of the Cerebellum

Article

Full-text available

Feb 2023
CEREBELLUM

The cerebellum is a key player in many brain functions and a major topic of neuroscience research. However, the cerebellar nuclei (CN), the main output structures of the cerebellum, are often overlooked. This neglect is because research on the cerebellum typically focuses on the cortex and tends to treat the CN as relatively simple output nuclei conveying an inverted signal from the cerebellar cortex to the rest of the brain. In this review, by adopting a nucleocentric perspective we aim to rectify this impression. First, we describe CN anatomy and modularity and comprehensively integrate CN architecture with its highly organized but complex afferent and efferent connectivity. This is followed by a novel classification of the specific neuronal classes the CN comprise and speculate on the implications of CN structure and physiology for our understanding of adult cerebellar function. Based on this thorough review of the adult literature we provide a comprehensive overview of CN embryonic development and, by comparing cerebellar structures in various chordate clades, propose an interpretation of CN evolution. Despite their critical importance in cerebellar function, from a clinical perspective intriguingly few, if any, neurological disorders appear to primarily affect the CN. To highlight this curious anomaly, and encourage future nucleocentric interpretations, we build on our review to provide a brief overview of the various syndromes in which the CN are currently implicated. Finally, we summarize the specific perspectives that a nucleocentric view of the cerebellum brings, move major outstanding issues in CN biology to the limelight, and provide a roadmap to the key questions that need to be answered in order to create a comprehensive integrated model of CN structure, function, development, and evolution.

Integrated cardio-behavioral responses to threat define defensive states

Article

Full-text available

Feb 2023
NAT NEUROSCI

Fear and anxiety are brain states that evolved to mediate defensive responses to threats. The defense reaction includes multiple interacting behavioral, autonomic and endocrine adjustments, but their integrative nature is poorly understood. In particular, although threat has been associated with various cardiac changes, there is no clear consensus regarding the relevance of these changes for the integrated defense reaction. Here we identify rapid microstates that are associated with specific behaviors and heart rate dynamics, which are affected by long-lasting macrostates and reflect context-dependent threat levels. In addition, we demonstrate that one of the most commonly used defensive behavioral responses—freezing as measured by immobility—is part of an integrated cardio-behavioral microstate mediated by Chx10⁺ neurons in the periaqueductal gray. Our framework for systematic integration of cardiac and behavioral readouts presents the basis for a better understanding of complex neural defensive states and their associated systemic functions.

Neurotransmitter content heterogeneity within an interneuron class shapes inhibitory transmission at a central synapse

Article

Full-text available

Jan 2023

Neurotransmitter content is deemed the most basic defining criterion for neuronal classes, contrasting with the intercellular heterogeneity of many other molecular and functional features. Here we show, in the adult mouse brain, that neurotransmitter content variegation within a neuronal class is a component of its functional heterogeneity. Golgi cells (GoCs), the well-defined class of cerebellar interneurons inhibiting granule cells (GrCs), contain cytosolic glycine, accumulated by the neuronal transporter GlyT2, and GABA in various proportions. By performing acute manipulations of cytosolic GABA and glycine supply, we find that competition of glycine with GABA reduces the charge of IPSC evoked in GrCs and, more specifically, the amplitude of a slow component of the IPSC decay. We then pair GrCs recordings with optogenetic stimulations of single GoCs, which preserve the intracellular transmitter mixed content. We show that the strength and decay kinetics of GrCs IPSCs, which are entirely mediated by GABA A receptors, are negatively correlated to the presynaptic expression of GlyT2 by GoCs. We isolate a slow spillover component of GrCs inhibition that is also affected by the expression of GlyT2, leading to a 56% decrease in relative charge. Our results support the hypothesis that presynaptic loading of glycine negatively impacts the GABAergic transmission in mixed interneurons, most likely through a competition for vesicular filling. We discuss how the heterogeneity of neurotransmitter supply within mixed interneurons like the GoC class may provide a presynaptic mechanism to tune the gain of microcircuits such as the granular layer, thereby expanding the realm of their possible dynamic behaviors.

A method to estimate the cellular composition of the mouse brain from heterogeneous datasets

Article

Full-text available

Dec 2022
PLOS COMPUT BIOL

The mouse brain contains a rich diversity of inhibitory neuron types that have been characterized by their patterns of gene expression. However, it is still unclear how these cell types are distributed across the mouse brain. We developed a computational method to estimate the densities of different inhibitory neuron types across the mouse brain. Our method allows the unbiased integration of diverse and disparate datasets into one framework to predict inhibitory neuron densities for uncharted brain regions. We constrained our estimates based on previously computed brain-wide neuron densities, gene expression data from in situ hybridization image stacks together with a wide range of values reported in the literature. Using constrained optimization, we derived coherent estimates of cell densities for the different inhibitory neuron types. We estimate that 20.3% of all neurons in the mouse brain are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. We find that our density estimations improve as more literature values are integrated. Our pipeline is extensible, allowing new cell types or data to be integrated as they become available. The data, algorithms, software, and results of our pipeline are publicly available and update the Blue Brain Cell Atlas. This work therefore leverages the research community to collectively converge on the numbers of each cell type in each brain region.

Model simulations unveil the structure-function-dynamics relationship of the cerebellar cortical microcircuit

Article

Full-text available

Nov 2022

The cerebellar network is renowned for its regular architecture that has inspired foundational computational theories. However, the relationship between circuit structure, function and dynamics remains elusive. To tackle the issue, we developed an advanced computational modeling framework that allows us to reconstruct and simulate the structure and function of the mouse cerebellar cortex using morphologically realistic multi-compartmental neuron models. The cerebellar connectome is generated through appropriate connection rules, unifying a collection of scattered experimental data into a coherent construct and providing a new model-based ground-truth about circuit organization. Naturalistic background and sensory-burst stimulation are used for functional validation against recordings in vivo, monitoring the impact of cellular mechanisms on signal propagation, inhibitory control, and long-term synaptic plasticity. Our simulations show how mossy fibers entrain the local neuronal microcircuit, boosting the formation of columns of activity travelling from the granular to the molecular layer providing a new resource for the investigation of local microcircuit computation and of the neural correlates of behavior. A computational modelling framework is developed, and used to reconstruct and simulate the entire cerebellar cortex, showing how mossy fibers entrain the neuronal microcircuit. This sheds light on local computation and neural correlates of behaviors

Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System

Article

Full-text available

Jun 2022

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model for studying active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modeling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was adequately connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behavior experimentally recorded in mice.

Candelabrum cells are ubiquitous cerebellar cortex interneurons with specialized circuit properties

Article

Full-text available

Jun 2022
NAT NEUROSCI

To understand how the cerebellar cortex transforms mossy fiber (MF) inputs into Purkinje cell (PC) outputs, it is vital to delineate the elements of this circuit. Candelabrum cells (CCs) are enigmatic interneurons of the cerebellar cortex that have been identified based on their morphology, but their electrophysiological properties, synaptic connections and function remain unknown. Here, we clarify these properties using electrophysiology, single-nucleus RNA sequencing, in situ hybridization and serial electron microscopy in mice. We find that CCs are the most abundant PC layer interneuron. They are GABAergic, molecularly distinct and present in all cerebellar lobules. Their high resistance renders CC firing highly sensitive to synaptic inputs. CCs are excited by MFs and granule cells and are strongly inhibited by PCs. CCs in turn primarily inhibit molecular layer interneurons, which leads to PC disinhibition. Thus, inputs, outputs and local signals converge onto CCs to allow them to assume a unique role in controlling cerebellar output.

Novel Cerebello-Amygdala Connections Provide Missing Link Between Cerebellum and Limbic System

Article

Full-text available

May 2022

The cerebellum is emerging as a powerful regulator of cognitive and affective processing and memory in both humans and animals and has been implicated in affective disorders. How the cerebellum supports affective function remains poorly understood. The short-latency (just a few milliseconds) functional connections that were identified between the cerebellum and amygdala—a structure crucial for the processing of emotion and valence—more than four decades ago raise the exciting, yet untested, possibility that a cerebellum-amygdala pathway communicates information important for emotion. The major hurdle in rigorously testing this possibility is the lack of knowledge about the anatomy and functional connectivity of this pathway. Our initial anatomical tracing studies in mice excluded the existence of a direct monosynaptic connection between the cerebellum and amygdala. Using transneuronal tracing techniques, we have identified a novel disynaptic circuit between the cerebellar output nuclei and the basolateral amygdala. This circuit recruits the understudied intralaminar thalamus as a node. Using ex vivo optophysiology and super-resolution microscopy, we provide the first evidence for the functionality of the pathway, thus offering a missing mechanistic link between the cerebellum and amygdala. This discovery provides a connectivity blueprint between the cerebellum and a key structure of the limbic system. As such, it is the requisite first step toward obtaining new knowledge about cerebellar function in emotion, thus fundamentally advancing understanding of the neurobiology of emotion, which is perturbed in mental and autism spectrum disorders.

Transcriptomic mapping uncovers Purkinje neuron plasticity driving learning

Article

Full-text available

May 2022
NATURE

Cellular diversification is critical for specialized functions of the brain including learning and memory1. Single-cell RNA sequencing facilitates transcriptomic profiling of distinct major types of neuron2–4, but the divergence of transcriptomic profiles within a neuronal population and their link to function remain poorly understood. Here we isolate nuclei tagged5 in specific cell types followed by single-nucleus RNA sequencing to profile Purkinje neurons and map their responses to motor activity and learning. We find that two major subpopulations of Purkinje neurons, identified by expression of the genes Aldoc and Plcb4, bear distinct transcriptomic features. Plcb4+, but not Aldoc+, Purkinje neurons exhibit robust plasticity of gene expression in mice subjected to sensorimotor and learning experience. In vivo calcium imaging and optogenetic perturbation reveal that Plcb4+ Purkinje neurons have a crucial role in associative learning. Integrating single-nucleus RNA sequencing datasets with weighted gene co-expression network analysis uncovers a learning gene module that includes components of FGFR2 signalling in Plcb4+ Purkinje neurons. Knockout of Fgfr2 in Plcb4+ Purkinje neurons in mice using CRISPR disrupts motor learning. Our findings define how diversification of Purkinje neurons is linked to their responses in motor learning and provide a foundation for understanding their differential vulnerability to neurological disorders. Subpopulations of Purkinje neurons display distinct transcriptomic responses and functions in associative learning.

The quest for multiscale brain modeling

Article

Jul 2022
TRENDS NEUROSCI

Addressing the multiscale organization of the brain, which is fundamental to the dynamic repertoire of the organ, remains challenging. In principle, it should be possible to model neurons and synapses in detail and then connect them into large neuronal assemblies to explain the relationship between microscopic phenomena, large-scale brain functions, and behavior. It is more difficult to infer neuronal functions from ensemble measurements such as those currently obtained with brain activity recordings. In this article we consider theories and strategies for combining bottom-up models, generated from principles of neuronal biophysics, with top-down models based on ensemble representations of network activity and on functional principles. These integrative approaches are hoped to provide effective multiscale simulations in virtual brains and neurorobots, and pave the way to future applications in medicine and information technologies.

Variations on the theme: focus on cerebellum and emotional processing

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