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Tc-99m-HMPAO perfusion SPECT scan data presented in surface rendering. The color scale is scaled relative to the patient's mean cerebral perfusion. Mean blood flow (72%) is in yellow. Color shifts occur at approximately every 0.5 SD (3%) relative to the patient's mean. Diffuse cortical hypoperfusion (green and blue) is clearly evident.
Source publication
While early efforts in psychiatry were focused on uncovering the neurobiological basis of psychiatric symptoms, they made little progress due to limited ability to observe the living brain. Today, we know a great deal about the workings of the brain; yet, none of this neurobiological awareness has translated into the practice of psychiatry. The cat...
Citations
... In clinical settings, fMRI has the potential to serve as a biomarker for psychiatric disorders characterized by emotional dysregulation, aiding in diagnosis and treatment planning (Henderson et al., 2020). For example, abnormalities in the PFC-amygdala circuitry have been observed in individuals with depression and anxiety disorders, highlighting potential targets for intervention. ...
Neuroimaging techniques have significantly advanced our understanding of emotional regulation by elucidating the neural mechanisms involved. This review synthesizes findings across multiple modalities—functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), Positron Emission Tomography (PET), and Magnetic Resonance Spectroscopy (MRS)—to highlight key insights into emotional processing. The prefrontal cortex (PFC), amygdala, hippocampus, and insula emerge as critical brain regions in regulating emotions. fMRI studies demonstrate increased PFC activity and decreased amygdala responses during effective emotion regulation, indicating top-down control mechanisms. EEG and MEG provide insights into the temporal dynamics of emotional responses, capturing rapid changes in neural activity during emotional tasks. PET and MRS studies reveal the neurochemical basis of emotional regulation, emphasizing the roles of neurotransmitters like serotonin and dopamine. Integration of multimodal approaches, such as fMRI-EEG and fMRI-PET, enhances our understanding by combining spatial, temporal, and neurochemical specificity. Challenges include methodological limitations and the need for diverse participant samples to improve generalizability. Future research should focus on improving spatial and temporal resolution, adopting longitudinal and ecologically valid designs, and fostering interdisciplinary collaborations. These advancements hold promise for developing personalized interventions and treatments for emotional disorders by leveraging neuroimaging biomarkers and understanding the neural underpinnings of emotional regulation.
... Recent initiatives have introduced various translational methods involving resting-state fMRI, such as the adoption of new transdisciplinary diagnostic tools in schizophrenia. For example, the abnormal patterns uncovered by resting-state fMRI results, such as thalamic-auditory cortexhippocampal connection defects or the abnormal corticalstriatal-cerebellar network connections identified in prior research, can serve as biomarkers for the preliminary diagnosis of schizophrenia 54,55 . Subsequent treatment with diagnostic medications, if successful in alleviating symptoms, can confirm the diagnosis of schizophrenia 56 . ...
Schizophrenia, a multifaceted mental disorder characterized by disturbances in thought, perception, and emotion, has been extensively investigated through resting-state fMRI, uncovering changes in spontaneous brain activity among those affected. However, a bibliometric examination regarding publication trends in resting-state fMRI studies related to schizophrenia is lacking. This study obtained relevant publications from the Web of Science Core Collection spanning the period from 1998 to 2022. Data extracted from these publications included information on countries/regions, institutions, authors, journals, and keywords. The collected data underwent analysis and visualization using VOSviewer software. The primary analyses included examination of international and institutional collaborations, authorship patterns, co-citation analyses of authors and journals, as well as exploration of keyword co-occurrence and temporal trend networks. A total of 859 publications were retrieved, indicating an overall growth trend from 1998 to 2022. China and the United States emerged as the leading contributors in both publication outputs and citations, with Central South University and the University of New Mexico being identified as the most productive institutions. Vince D. Calhoun had the highest number of publications and citation counts, while Karl J. Friston was recognized as the most influential author based on co-citations. Key journals such as Neuroimage, Schizophrenia Research, Schizophrenia Bulletin, and Biological Psychiatry played pivotal roles in advancing this field. Recent popular keywords included support vector machine, antipsychotic medication, transcranial magnetic stimulation, and related terms. This study systematically synthesizes the historical development, current status, and future trends in resting-state fMRI research in schizophrenia, offering valuable insights for future research directions.
... Diagnostic decisions are generally supported with objective measures such as laboratory tests or neuroimaging approaches. At this point, the usage of functional neuroimaging approaches as diagnostic tools is still widely being discussed (Henderson et al. 2020). Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), Positron Emission Tomography (PET) and Functional Near Infrared Spectroscopy (fNIRS) are the most common functional neuroimaging approaches that are used to disclose potential biomarkers to discriminate psychiatric or neurological disorders having common symptoms or these disorders from healthy individuals (Nour et al. 2022). ...
Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n = 12), attention deficit and hyperactivity disorder (n = 7), and autism spectrum disorder (n = 6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM = 20) (DL = 10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n = 11) and ΔHbO-based functional connections (n = 11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.
... Meanwhile, some studies have found reduced perfusion in different brain regions in patients with mild CI or Alzheimer's disease (Binnewijzend et al., 2013;Dai et al., 2009;Lou et al., 2016). In Alzheimer's disease and Parkinson's disease, some studies have shown that positron emission tomographycomputed tomography (CT) can detect brain metabolism decline and correlate the severity of psychiatric symptoms with metabolic changes (Carey et al., 2021;Henderson et al., 2020;Wolinsky et al., 2018). ...
Purpose
Glioma patients have varying degrees of psychiatric symptoms, which severely affect the quality of life of patients and their families. The present study investigated the correlation between preoperative psychiatric symptoms and local cerebral perfusion parameters of in glioma patients.
Patients and methods
The depression, anxiety, and cognitive impairment (CI) scores of 39 patients were assessed separately, and all of the patients underwent a preoperative perfusion computed tomography scan.
Results
This study found that: (1) The incidence of preoperative symptoms of depression, anxiety, and CI was 46.15%, 48.72%, and 25.64%, respectively. (2) Cerebral blood volume (CBV) (lesion‐sided [LS] occipital lobe white matter [WM] and parietal lobe WM and normal‐sided temporal lobe WM), permeability surface (PS) (LS temporal lobe gray matter [GM] and parietal lobe WM) in the depression group were significantly decreased (p < .05). (3) CBV (LS occipital lobe WM), cerebral blood flow (LS parietal lobe GM, centrum ovale and frontal lobe WM and normal‐sided frontal lobe WM, temporal lobe WM and parietal lobe WM), and mean transition time (MTT) (normal‐sided frontal lobe WM and temporal lobe WM) in the anxiety group were significantly increased (p < .05). (4) CBV (LS temporal lobe GM), MTT (LS anterior limb of internal capsule), and PS (LS thalamus) in the CI group were significantly increased (p < .05).
Conclusion
This study showed that glioma patients had different levels of psychological distress in glioma patients before surgery, which may be related to the changes in brain perfusion caused by the tumor.
... In contrast to induction, we are inferring S → D. Inductive inference differs from deductive inference because although the "direction" is D → S in deductive inference, the truth of D and S are absolute; for example, in Fig. 2, deductive inference would assert that it is necessarily true that if a person has psychosis, they definitely have abnormal beliefs (cf. the probabilistic interpretation afforded by inductive reasoning). To re-use the example of making a diagnosis, it is clear that psychiatric diagnoses have "many-to-many" mappings with the underlying biology [38][39][40] , the probabilistic nature of psychiatric diagnosis (i.e. the mapping of signs/ symptoms to diagnoses) has long been recognised 41 and consequently, influenced the dimensional characterisation of disorders 42 . Here, we suggest that an abductive presentation would be most suitable. ...
The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what “explainability” means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term “explainability” in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to “ground” in our understanding of how algorithms and models operate and are used more consistently in the literature. We describe the TIFU (Transparency and Interpretability For Understandability) framework and examine how this applies to the landscape of AI/ML in mental health research. We argue that the need for understandablity is heightened in psychiatry because data describing the syndromes, outcomes, disorders and signs/symptoms possess probabilistic relationships to each other—as do the tentative aetiologies and multifactorial social- and psychological-determinants of disorders. If we develop and deploy AI/ML models, ensuring human understandability of the inputs, processes and outputs of these models is essential to develop trustworthy systems fit for deployment.
... Indeed, the vast majority of fMRI studies conducted between 2007 and 2011 have adopted a localizationist framework, and only 11 percent of these studies tested explicitly defined cognitive models (Tressoldi et al., 2012). As an aside, it is noteworthy that there is a conspicuous absence of neuroimaging markers in the major neuropsychiatric diagnostic classification manual, the DSM-5 (APA, 2013), and a lack of consensus as to whether this omission will change in the near future (Aydin et al., 2019;Etkin, 2019;First et al., 2018;Henderson et al., accumulated shortcomings within the scientific literature have cast doubt over the integrity of the whole field (see John et al., 2012;Simmons et al., 2011). In neuroimaging studies (and fMRI studies in particular), evidence of this replication and reproducibility crisis has included reliance on flawed statistical procedures, small sample sizes and career incentive structures that emphasize rapid publication of questionable findings, while disincentivizing studies that report null findings (Button et al., 2013;Fanelli, 2012;John et al., 2012;Ritchie, 2020;Szucs and Ioannidis, 2017;Turner et al., 2018;Weinberger and Radulescu, 2016). ...
... Virtual Reality is an immersive technology and the use of this technology is everywhere nowadays such as in healthcare, education, travel and tourism, military, entertainment, engineering, marketing and advertisements [1]. The study of this article is based on virtual reality and augmented reality in the medical and healthcare sector. ...
... In 2020, the use of virtual reality tool that's name is oculus rift device, through this device they proposed the virtual scenario for the patients give therapies according to their health conditions [7]. Then if the exercises are doing very well in properly manner then they apply electrooculography and connect with brain computer interface system so that they can get information about their problem to resolve the issues [1]. In 2021, there were various technologies has been transformed for the healthcare department and also trained several professionals with the enhancement of virtual reality technologies. ...
... Virtual and Augmented Realities are utilizing a variety of overlays and digital immersion on the actual world with which users can interact with. Extended Reality (XR) is the term that refers to both virtual and actual environment that interacts with the users with the use of computed and wearable technology [1,3]. Nowadays, tablets and mobile phones are the most popular mediums of Augmented Reality and via camera, some 3D applications which are used to show digital content into the natural environment and VRheadsets are also used to display 3600 videos. ...
... Therefore, if the somatosensory-related brain regions are impaired, somatosensory processing will be impeded, affecting the development of working memory, attention, and visual-motor integration. Abnormal activation of these somatosensory cortices has been found in ADHD [70,71]. Combined with previous research, our results suggested that tactile and proprioception may affect inattention symptoms through working memory. ...
Attention-deficit/hyperactivity disorder (ADHD) is often accompanied by executive function deficits and functional alterations in sensory integration. The present study aimed to investigate the relationship between ADHD core symptoms, executive function, and sensory integration in children with ADHD. A total of 228 children with ADHD were recruited for our study. The Sensory Organization Test (SOT) and Child Sensory Integration Scale (CSIS) evaluated the sensory integration ability from lab-based and scaled-based perspectives, respectively. Three core components of executive functions (inhibition, working memory, and set-shifting) were assessed using both lab-based tests and the relevant factors from the behavior rating inventory of executive function (BRIEF). Partial correlation analysis was performed to explore the correlation of sensory integration with EF and ADHD core symptoms. Based on the observed significant correlation, bootstrap analyses were further conducted to explore the potential mediating effect of EF on the relationship between sensory integration and ADHD core symptoms. ADHD symptoms and EF were significantly correlated with CSIS scores; no factors were significantly correlated with SOT performance. In detail, the vestibular-balance score was negatively correlated with both inattention and hyperactivity/impulsivity symptoms, while the hyper-sensory and proprioception scores were negatively correlated with only inattention symptoms. For the scaled-based EF, vestibular-balance was negatively correlated with inhibition and working memory, and the hyper-sensory score was negatively correlated with shift factor. No correlation was found for the lab-based EF tests. The subsequent mediation analysis found that inhibition partially mediated the relationship between vestibular balance and hyperactivity/impulsivity symptoms. Working memory completely mediated the relationship between vestibular-balance, hyper-sensory, proprioception, and inattention symptoms. These results were well validated in an independent sample. Our present findings demonstrated that the functional alteration in basic sensory integration might be associated with impairments of executive functions and then lead to the behavioral expression of ADHD. The present findings might provide a new perspective to understand the occurrence of ADHD symptoms and potential precise intervention methods.
... While the American Psychiatric Association (APA) initially embraced SPECT neuroimaging (holding seminars and workshops on its use per, Amen and Easton), now the APA and psychiatrists, in general, vilify SPECT and clinicians who utilize it (1). The untenable position of the APA (2), mired in the committee-created artificial diagnostic constructs of the Diagnostic and Statistical Manual (DSM) system (3), cannot be reconciled with the current level of neurophysiological evidence of the underpinnings of psychiatric conditions, as revealed by perfusion SPECT, quantitative electroencephalographalogram (qEEG), arterial spin echo, and functional MRI (fMRI) studies [ (4,5); Pavel et al. (A)]. Neurology as a field has taken a similar position, rejecting perfusion SPECT neuroimaging as "old", "inaccurate", or "experimental". ...
... Amen et al. described a potential biomarker for ADHD in a large population (N = 1,006) defined by DSM-IV criteria, detailed clinical history, and the Structure Clinical Interview for Diagnosis (SCID) who were compared to a control group that did not meet DSM-IV criteria for any psychiatric disorder. They found that hypoperfusion in the medial anterior prefrontal (orbitofrontal) cortices, anterior cingulate gyri, bilateral temporal cortices, and cerebellar subregions 8 and 9 were highly predictive of ADHD with a sensitivity of 100% and specificity of 100% ( Furthermore, prior studies and reviews (4,5,(8)(9)(10)(11)(12)(13)(14) support the use of SPECT in the evaluation of TBI. ...
... In all cases, they demonstrated improvement in clinical symptoms and neurophysiological function based on pre-and post-treatment SPECT scans. Thornton (4,5,(12)(13)(14)(19)(20)(21). ...
... This facilitates comfort during the scanning process, reduces motion, and eliminates excess unbound radiopharmaceutical (HMPAO and ECD are predominately cleared by the kidneys). Further discussion of radiation safety is beyond the scope of this article but are reviewed at length elsewhere (31)(32)(33). ...
... we can see perfusion SPECT neuroimaging can serve as an objective biomarker of response to treatment (144,145), comorbidity (30,33,82,84,133), inherently difficult symptoms to objectify, such as pain (147), and of diagnostic masqueraders as described herein and elsewhere (30,33,84,133,148). ...
... we can see perfusion SPECT neuroimaging can serve as an objective biomarker of response to treatment (144,145), comorbidity (30,33,82,84,133), inherently difficult symptoms to objectify, such as pain (147), and of diagnostic masqueraders as described herein and elsewhere (30,33,84,133,148). ...
Brain perfusion single photon emission computed tomography (SPECT) scans were initially developed in 1970s. A key radiopharmaceutical, hexamethylpropyleneamine oxime (HMPAO), was not stabilized until 1993 and most early SPECT scans were performed on single-head gamma cameras. These early scans were of inferior quality. In 1996, the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (TTASAAN) issued a report regarding the use of SPECT in the evaluation of neurological disorders. This two-part series explores the policies and procedures related to perfusion SPECT functional neuroimaging. In Part I, the comparison between the quality of the SPECT scans and the depth of the data for key neurological and psychiatric indications at the time of the TTASAAN report vs. the intervening 25 years were presented. In Part II, the technical aspects of perfusion SPECT neuroimaging and image processing will be explored. The role of color scales will be reviewed and the process of interpreting a SPECT scan will be presented. Interpretation of a functional brain scans requires not only anatomical knowledge, but also technical understanding on correctly performing a scan, regardless of the scanning modality. Awareness of technical limitations allows the clinician to properly interpret a functional brain scan. With this foundation, four scenarios in which perfusion SPECT neuroimaging, together with other imaging modalities and testing, lead to a narrowing of the differential diagnoses and better treatment. Lastly, recommendations for the revision of current policies and practices are made.
