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Increased baseline activity (noise) in depression microcircuit models. a. Depression microcircuits were modeled according to gene expression data, with -40% synaptic and tonic inhibition conductance from SST interneurons onto the other neuron types. b. Relative tonic inhibition conductance onto the different neuron types in depression microcircuits, compared to healthy microcircuits (dashed line). c. Example raster plot of simulated baseline spiking in a healthy microcircuit model (top) and depression microcircuit model (bottom). d. Increased intrinsic Pyr neuron firing in depression microcircuit models (n = 200 randomized microcircuits per condition, purple, p < 0.05, Cohen's d = 7.06, healthy level in dark grey). The relationship between SST interneuron inhibition reduction and baseline Pyr neuron firing rate was approximately linear. e. Increased interneuron baseline firing rates in depression microcircuit models (p < 0.05).
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Cortical processing depends on finely-tuned excitatory and inhibitory connections in neuronal microcircuits. In major depressive disorder (depression), a disrupted balance due to weaker inhibition by somatostatin-expressing interneurons is implicated in cognitive deficits and rumination symptoms. Here, we tested the impact of reduced somatostatin i...
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... next modelled human depression microcircuits by reducing SST interneuron synaptic and tonic inhibition conductance onto the different neuron types by 40%, according to postmortem gene expression data in depression ( Fig. 3a -b). We then compared the baseline activity in healthy and depression microcircuits, across all neurons of each type. The average baseline firing rate of Pyr neurons was significantly higher in depression microcircuits compared to healthy microcircuits ( Fig. 3c -d, healthy: 0.77 ± 0.05 Hz, depression: 1.20 ± 0.07 Hz, n = 200 randomized ...
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... neuron types by 40%, according to postmortem gene expression data in depression ( Fig. 3a -b). We then compared the baseline activity in healthy and depression microcircuits, across all neurons of each type. The average baseline firing rate of Pyr neurons was significantly higher in depression microcircuits compared to healthy microcircuits ( Fig. 3c -d, healthy: 0.77 ± 0.05 Hz, depression: 1.20 ± 0.07 Hz, n = 200 randomized microcircuits, t-test p < 0.05, Cohen's d = 7.06). Next, we quantified the effect of different levels of SST interneuron inhibition reduction on baseline rates of Pyr neurons by simulating microcircuits with 0 -100% reduction compared to the healthy level. The ...
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... p < 0.05, Cohen's d = 7.06). Next, we quantified the effect of different levels of SST interneuron inhibition reduction on baseline rates of Pyr neurons by simulating microcircuits with 0 -100% reduction compared to the healthy level. The baseline firing of Pyr neurons increased approximately linearly with reduced SST interneuron inhibition (Fig. ...
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Cortical processing depends on finely tuned excitatory and inhibitory connections in neuronal microcircuits. Reduced inhibition by somatostatin-expressing interneurons is a key component of altered inhibition associated with treatment-resistant major depressive disorder (depression), which is implicated in cognitive deficits and rumination, but the...
Citations
... In terms of morphology, Pyr neurons are larger in humans and have longer and more complex dendrites which affect input integration [40,41]. The increased availability of human neuronal and synaptic connectivity data [38,39,42] has enabled the generation of detailed models of human cortical microcircuits [43,44]. These constraints can be used to simulate microcircuit activity in health and disease as well as local microcircuit-generated EEG signals, and link microcircuit mechanisms to EEG signatures [43,45,46]. ...
... The increased availability of human neuronal and synaptic connectivity data [38,39,42] has enabled the generation of detailed models of human cortical microcircuits [43,44]. These constraints can be used to simulate microcircuit activity in health and disease as well as local microcircuit-generated EEG signals, and link microcircuit mechanisms to EEG signatures [43,45,46]. Using models constrained by human data enable predicting effects relevant to human disorders and identifying important clinically-relevant EEG biomarkers in schizophrenia. ...
... We modeled detailed human PFC microcircuit models by adapting our previous models of human cortical layer 2/3 microcircuits [43], which consisted of 1000 connected neurons distributed randomly in a 500x500x950 μm 3 volume, situated 250µm -1200 m below the pia (corresponding to human L2/3) [41]. The neuron models reproduce the firing, dendritic sag current, and synaptic properties measured in human neurons. ...
Reduced cortical inhibition by parvalbumin-expressing (PV) interneurons has been associated with impaired cortical processing in the prefrontal cortex (PFC) and altered EEG signals such as oddball mismatch negativity (MMN) in schizophrenia. However, establishing the link between reduced PV interneuron inhibition and reduced MMN experimentally in humans is currently not possible. To overcome these challenges, we used detailed computational models of human PFC microcircuits, and modeled schizophrenia microcircuits by integrating gene-expression data from schizophrenia patients indicating reduced PV interneuron inhibition output and NMDA input. We simulated spiking activity and EEG in microcircuits with different levels of reduced PV interneuron mechanisms and showed that a double effect of the reduction indicated by gene-expression led to a significantly reduced MMN amplitude as seen in Schizophrenia patients, whereas a single effect produced a smaller reduction in MMN that matched the magnitude seen in patients at high-risk of schizophrenia. In addition, we showed that simulated resting EEG of schizophrenia microcircuits exhibited a right shift from alpha to beta frequencies. Our study thus links the level of reduced PV interneuron inhibition to distinct EEG biomarkers that can serve to better stratify different severities of schizophrenia and improve the early detection using non-invasive brain signals.
... SST interneurons provide synaptic and tonic inhibition onto the apical dendrites of Pyr neurons [19,20], and mediate lateral inhibition in the cortex [8,21,22], particularly during periods of quiet, resting wakefulness (resting state) [23]. Accordingly, previous studies indicate that reduced SST inhibition in depression increases baseline activity of Pyr neurons [9,24]. ...
... Thus, reduced SST inhibition in depression may affect EEG low-frequency power [28]. We recently showed that a 40% reduction in SST inhibition, estimated from post-mortem tissue studies of depression patients [13], significantly increased baseline Pyr spike rate in simulated human microcircuits [24]. ...
... However, experimental methods are limited in their ability to characterize the effects of the cellular changes in depression on human brain signals in vivo, thus meriting the use of computational models. The increased availability of human neuronal electrical and synaptic connectivity data [22,36,37], provides important constraints on detailed models of human cortical microcircuitry [24] which can be used to simulate microcircuit activity in health and disease, including local circuit-generated EEG signals [38][39][40]. ...
Major depressive disorder (depression) is a complex condition that involves multiple physiological mechanisms, spanning a range of spatial scales. Altered cortical inhibition is associated with treatment-resistant depression, and reduced dendritic inhibition by somatostatin-expressing (SST) interneurons has been strongly implicated in this aspect of the pathology. However, whether the effects of reduced SST inhibition on microcircuit activity have signatures detectible in electroencephalography (EEG) signals remains unknown. We used detailed models of human cortical layer 2/3 microcircuits with normal or reduced SST inhibition to simulate resting-state activity together with EEG signals in health and depression. We first show that the healthy microcircuit models exhibit emergent key features of resting-state EEG. We then simulated EEG from depression microcircuits and found a significant power increase in theta, alpha and low beta frequencies (4 - 15 Hz). Following spectral decomposition, we show that the power increase involved a combination of aperiodic broadband component, and a periodic theta and low beta components. Neuronal spiking showed a spike preference for the phase preceding the EEG trough, which did not differ between conditions. Our study thus used detailed computational models to identify EEG biomarkers of reduced SST inhibition in human cortical microcircuits in depression, which may serve to improve the diagnosis and stratification of depression subtypes, and in monitoring the effects of pharmacological modulation of inhibition for treating depression.
... Furthermore, SST-KO mice exhibit depressive-and anxiety-like behaviors along with reductions of BDNF and GAD67 expression in GABA interneurons and elevated corticosterone blood levels (Lin and Sibille, 2015). By compiling human cellular, synaptic, and gene expression data from recent studies, Yao et al. (2021) generated computational models of human layer II/III local circuit. Among these models, they created a depression microcircuit model by reducing SST inhibition according to previous gene expression data from MDD patients (Seney et al., 2015). ...
... Among these models, they created a depression microcircuit model by reducing SST inhibition according to previous gene expression data from MDD patients (Seney et al., 2015). In this model, a reduction in SST inhibition leads to an increase in the pyramidal firing rate, increasing baseline activity, disrupting signal-to-noise ratio, and the increasing probabilities of both false signal detection and failed signal detection over background noise (Yao et al., 2021). ...
Despite decades of research on the neurobiology of major depressive disorder (MDD), the mechanisms underlying its expression remain unknown. The medial prefrontal cortex (mPFC), a hub region involved in emotional processing and stress response elaboration, is highly impacted in MDD patients and animal models of chronic stress. Recent advances showed alterations in the morphology and activity of mPFC neurons along with profound changes in their transcriptional programs. Studies at the circuitry level highlighted the relevance of deciphering the contributions of the distinct prefrontal circuits in the elaboration of adapted and maladapted behavioral responses in the context of chronic stress. Interestingly, MDD presents a sexual dimorphism, a feature recognized in the molecular field but understudied on the circuit level. This review examines the recent literature and summarizes the contribution of the mPFC circuitry in the expression of MDD in males and females along with the morphological and functional alterations that change the activity of these neuronal circuits in human MDD and animal models of depressive-like behaviors.
Introduction
Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules.
Methods
We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly.
Results
Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions.
Discussion
These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin’s proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.
