BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.

Arterial spin labelling magnetic resonance imaging and perfusion patterns in neurocognitive and other mental disorders: a systematic review.

We reviewed 33 original research studies assessing brain perfusion, using consensus guidelines from a "white paper" issued by the International Society for Magnetic Resonance in Medicine Perfusion Study Group and the European Cooperation in Science and Technology Action BM1103 ("Arterial Spin Labelling Initiative in Dementia"; https://www.cost.eu/actions/BM1103/ ). The studies were published between 2011 and 2023 and included participants with subjective cognitive decline plus; neurocognitive disorders, including mild cognitive impairment (MCI), Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD), dementia with Lewy bodies (DLB) and vascular cognitive impairment (VCI); as well as schizophrenia spectrum disorders, bipolar and major depressive disorders, autism spectrum disorder, attention-deficit/hyperactivity disorder, panic disorder and alcohol use disorder. Hypoperfusion associated with cognitive impairment was the major finding across the spectrum of cognitive decline. Regional hyperperfusion also was reported in MCI, AD, frontotemporal dementia phenocopy syndrome and VCI. Hypoperfused structures found to aid in diagnosing AD included the precunei and adjacent posterior cingulate cortices. Hypoperfused structures found to better diagnose patients with FTLD were the anterior cingulate cortices and frontal regions. Hypoperfusion in patients with DLB was found to relatively spare the temporal lobes, even after correction for partial volume effects. Hyperperfusion in the temporal cortices and hypoperfusion in the prefrontal and anterior cingulate cortices were found in patients with schizophrenia, most of whom were on medication and at the chronic stage of illness. Infratentorial structures were found to be abnormally perfused in patients with bipolar or major depressive disorders. Brain perfusion abnormalities were helpful in diagnosing most neurocognitive disorders. Abnormalities reported in VCI and the remaining mental disorders were heterogeneous and not generalisable.

Neurobiological evidence and criminal competencies.

Neuroimaging and other neurobiological evidences are increasingly introduced in criminal litigation, especially when a neuropsychiatric disorder is suspected. Evaluations of criminal competencies are the most common type of criminal forensic assessment in forensic psychiatry and psychology. Given this, it is critical for forensic evaluators to understand how neuropsychiatric disorders may affect a defendant's criminal competencies and how neurobiological data may be used in competency determinations. This paper reviews the use of neurobiological data, particularly neuroimaging, while considering the limitations and potential misuse of such data in criminal competency evaluations.

Brain Imaging in Routine Psychiatric Practice.

Technologies in the 21st century provide increasingly detailed and accurate maps of brain structure and function. So why don't psychiatrists order brain imaging on all our patients? Here we briefly review major neuroimaging methods and some of their findings in psychiatry. As clinicians and neuroimaging researchers, we are eager to bring brain imaging into daily clinical practice. However, to be clinically useful, any test in medicine must demonstrate adequate test statistics, and show proven benefits that outweigh its risks and costs. In 2024, beyond certain limited circumstances, we have no imaging tests that can meet those standards to provide diagnosis or guide treatment. This cold fact explains why for most psychiatric patients, neuroimaging is not currently recommended by professional organizations or the National Institute of Mental Health.

[Neuroimaging-based study of scalp acupuncture targets for common psychiatric diseases].

Scalp acupuncture is a unique acupuncture method, developed based on the cerebral cortex localization. Neuroimaging technology enables the combination of contemporary brain science findings with the studies of scalp stimulation sites. In this study, based on the neuroimaging literature retrieved from Neurosynth platform, the scalp stimulation targets of common psychiatric diseases are developed, which provides the stimulation target protocol of scalp acupuncture for anxiety, bipolar disorder, major depressive disorder and post-traumatic stress disorder. The paper introduces the functions of the brain areas that are involved in each target and closely related to the diseases, and lists the therapeutic methods of common acupuncture and scalp acupuncture for each disease so as to provide the references for clinical practice. These targets can be used not only for the stimulation of scalp acupuncture, but also for the different neuromodulation techniques to treat related diseases.

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis.

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.

Elevating the field for applying neuroimaging to individual patients in psychiatry.

Although neuroimaging has been widely applied in psychiatry, much of the exuberance in decades past has been tempered by failed replications and a lack of definitive evidence to support the utility of imaging to inform clinical decisions. There are multiple promising ways forward to demonstrate the relevance of neuroimaging for psychiatry at the individual patient level. Ultra-high field magnetic resonance imaging is developing as a sensitive measure of neurometabolic processes of particular relevance that holds promise as a new way to characterize patient abnormalities as well as variability in response to treatment. Neuroimaging may also be particularly suited to the science of brain stimulation interventions in psychiatry given that imaging can both inform brain targeting as well as measure changes in brain circuit communication as a function of how effectively interventions improve symptoms. We argue that a greater focus on individual patient imaging data will pave the way to stronger relevance to clinical care in psychiatry. We also stress the importance of using imaging in symptom-relevant experimental manipulations and how relevance will be best demonstrated by pairing imaging with differential treatment prediction and outcome measurement. The priorities for using brain imaging to inform psychiatry may be shifting, which compels the field to solidify clinical relevance for individual patients over exploratory associations and biomarkers that ultimately fail to replicate.

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders.

BACKGROUND: Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. METHODS: We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV). RESULTS: Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction. CONCLUSIONS: Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Psychiatric disorders have different prevalence between sexes, with women being twice as likely to develop depression and anxiety across the lifespan. Previous studies have investigated sex differences in brain structure that might contribute to this prevalence but have mostly focused on a single-structure level, potentially overlooking the interplay between brain regions. Sex differences in structures responsible for emotional regulation (limbic system), affected in many psychiatric disorders, have been previously reported. Here, we apply a machine learning model to obtain an estimate of brain sex for each participant based on the volumes of multiple brain regions. Particularly, we compared the estimates obtained with a model based solely on limbic structures with those obtained with a non-limbic model (entire brain except limbic structures) and a whole brain model. To investigate the genetic determinants of the models, we assessed the heritability of the estimates between identical twins and fraternal twins. The estimates of all our models were heritable, suggesting a genetic component contributing to brain sex. Finally, to investigate the association with mental health, we compared brain sex estimates in healthy subjects and in a depressed population. We found an association between depression and brain sex in females for the limbic model, but not for the non-limbic model. No effect was found in males. Overall, our results highlight the potential utility of machine learning models of brain sex based on relevant structures to better understand the link between sex differences in the brain and mental disorders.

[Neuroimaging-based study of scalp acupuncture targets for common psychiatric diseases (Part 2)].

In this study, based on the neuroimaging literature Meta analysis retrieved from Neurosynth platform, the scalp stimulation targets for common psychiatric diseases are developed, which provided the stimulation target protocols of scalp acupuncture for attention deficit hyperactivity disorder, autism spectrum disorder, obsessive-compulsive disorder and schizophrenia. The paper introduces the functions of the brain areas that are involved in each target and closely related to the diseases, and lists the therapeutic methods of common acupuncture/scalp acupuncture and common neuromodulation methods for each disease so as to provide the references for clinical practice. Based on the study results above, the paper further summarizes the overlapped stimulation targets undergoing the intervention with scalp acupuncture for common psychiatric diseases, and the potential relationship between these stimulation targets and treatments with acupuncture and moxibustion.

Generative AI for precision neuroimaging biomarker development in psychiatry.

The explosion of generative AI offers promise for neuroimaging biomarker development in psychiatry, but effective adoption of AI methods requires clarity with respect to specific applications and challenges. These center on dataset sizes required to robustly train AI models along with feature selection that capture neural signals relevant to symptom and treatment targets. Here we discuss areas where generative AI could improve quantification of robust and reproducible brain-to-symptom associations to inform precision psychiatry applications, especially in the context of drug discovery. Finally, this communication discusses some challenges that need solutions for generative AI models to advance neuroimaging biomarkers in psychiatry.

Potential dopaminergic deficit in patients with geriatric psychiatric disorders as revealed by DAT-SPECT: a cross-sectional study.

BACKGROUND: It has been reported that patients with geriatric psychiatric disorders include many cases of the prodromal stages of neurodegenerative diseases. Abnormal 123I-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl) nortropane dopamine transporter single-photon emission computed tomography (DAT-SPECT) reveals a nigrostriatal dopaminergic deficit and is considered useful to detect dementia with Lewy bodies and Parkinson's disease as well as progressive supranuclear palsy and corticobasal degeneration. We aimed to determine the proportion of cases that are abnormal on DAT-SPECT in patients with geriatric psychiatric disorders and to identify their clinical profile. METHODS: The design is a cross-sectional study. Clinical findings of 61 inpatients aged 60 years or older who underwent DAT-SPECT and had been diagnosed with psychiatric disorders, but not neurodegenerative disease or dementia were analysed. RESULTS: 36 of 61 (59%) had abnormal results on DAT-SPECT. 54 of 61 patients who had DAT-SPECT (89%) had undergone 123I-metaiodobenzylguanidine myocardial scintigraphy (123I-MIBG scintigraphy); 12 of the 54 patients (22.2%) had abnormal findings on 123I-MIBG scintigraphy. There were no cases that were normal on DAT-SPECT and abnormal on 123I-MIBG scintigraphy. DAT-SPECT abnormalities were more frequent in patients with late-onset (55 years and older) psychiatric disorders (69.0%) and depressive disorder (75.7%), especially late-onset depressive disorder (79.3%). CONCLUSION: Patients with geriatric psychiatric disorders include many cases showing abnormalities on DAT-SPECT. It is suggested that these cases are at high risk of developing neurodegenerative diseases characterised by a dopaminergic deficit. It is possible that patients with geriatric psychiatric disorders with abnormal findings on DAT-SPECT tend to show abnormalities on DAT-SPECT first rather than on 123I-MIBG scintigraphy.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration.

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

Functional neuroimaging in patients with catatonia: A systematic review.

BACKGROUND: Catatonia is a challenging and heterogeneous neuropsychiatric syndrome of motor, affective and behavioral dysregulation which has been associated with multiple disorders such as structural brain lesions, systemic diseases, and psychiatric disorders. This systematic review summarized and compared functional neuroimaging abnormalities in catatonia associated with psychiatric and medical conditions. METHODS: Using PRISMA methods, we completed a systematic review of 6 databases from inception to February 7th, 2024 of patients with catatonia that had functional neuroimaging performed. RESULTS: A total of 309 studies were identified through the systematic search and 62 met the criteria for full-text review. A total of 15 studies reported patients with catatonia associated with a psychiatric disorder (n = 241) and one study reported catatonia associated with another medical condition, involving patients with N-methyl-d-aspartate receptor antibody encephalitis (n = 23). Findings varied across disorders, with hyperactivity observed in areas like the prefrontal cortex (PFC), the supplementary motor area (SMA) and the ventral pre-motor cortex in acute catatonia associated to a psychiatric disorder, hypoactivity in PFC, the parietal cortex, and the SMA in catatonia associated to a medical condition, and mixed metabolic activity in the study on catatonia linked to a medical condition. CONCLUSION: Findings support the theory of dysfunction in cortico-striatal-thalamic, cortico-cerebellar, anterior cingulate-medial orbitofrontal, and lateral orbitofrontal networks in catatonia. However, the majority of the literature focuses on schizophrenia spectrum disorders, leaving the pathophysiologic characteristics of catatonia in other disorders less understood. This review highlights the need for further research to elucidate the pathophysiology of catatonia across various disorders.

Building diagnostic neuroimaging biomarkers for psychiatric disorders using reverse inference approaches: A viable route?

The advent of structural magnetic resonance imaging (sMRI) at the end of the 20th century opened the way toward a deeper understanding of the neurophysiology of psychiatric disorders, substantiating regional structural abnormalities underlying this group of clinical conditions. However, despite abundant and flourishing scientific research, sMRI methodologies are not currently integrated into daily diagnostic practice. One reason behind this failed translation may be the prevailing approach to logical reasoning in neuroimaging: The forward inference via frequentist-based statistics. This reasoning prevents clinicians from obtaining information about the selectivity of results, which are therefore of limited use regarding the definition of biomarkers and refinement of diagnostic processes. Recently, another type of inferential approach has started to emerge in the neuroimaging field: The reverse inference via Bayesian statistics. Here, we introduce the key concepts of this approach, with a particular emphasis on the clinical sMRI environment. We survey recent findings showing significant potential for clinical translation. Clinical opportunities and challenges for developing reverse inference-based neural markers for psychiatry are also discussed. We propose that a systematic sharing of imaging data across the human brain mapping community is an essential first step toward a paradigmatic clinical shift. We conclude that a defined synergy between forward-based and reverse-based sMRI research can illuminate current discussions on diagnostic brain markers, offering clarity on key issues and fostering new tailored diagnostic avenues.

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders.

Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.

Neuromelanin-sensitive magnetic resonance imaging in the study of mental disorder: A systematic review.

Dopamine and norepinephrine are implicated in the pathophysiology of mental disorders, but non-invasive study of their neuronal function remains challenging. Recent research suggests that neuromelanin-sensitive magnetic resonance imaging (NM-MRI) techniques may overcome this limitation by enabling the non-invasive imaging of the substantia nigra (SN)/ ventral tegmental area (VTA) dopaminergic and locus coeruleus (LC) noradrenergic systems. A review of 19 studies that met the criteria for NM-MRI application in mental disorders found that despite the use of heterogeneous sequence parameters and metrics, nearly all studies reported differences in contrast ratio (CNR) of LC or SN/VTA between patients with mental disorders and healthy controls. These findings suggest that NM-MRI is a valuable tool in psychiatry, but the differences in sequence parameters across studies hinder comparability, and a standardized analysis pipeline is needed to improve the reliability of results. Further research using standardized methods is needed to better understand the role of dopamine and norepinephrine in mental disorders.

Normative modelling of molecular-based functional circuits captures clinical heterogeneity transdiagnostically in psychiatric patients.

Advanced methods such as REACT have allowed the integration of fMRI with the brain's receptor landscape, providing novel insights transcending the multiscale organisation of the brain. Similarly, normative modelling has allowed translational neuroscience to move beyond group-average differences and characterise deviations from health at an individual level. Here, we bring these methods together for the first time. We used REACT to create functional networks enriched with the main modulatory, inhibitory, and excitatory neurotransmitter systems and generated normative models of these networks to capture functional connectivity deviations in patients with schizophrenia, bipolar disorder (BPD), and ADHD. Substantial overlap was seen in symptomatology and deviations from normality across groups, but these could be mapped into a common space linking constellations of symptoms through to underlying neurobiology transdiagnostically. This work provides impetus for developing novel biomarkers that characterise molecular- and systems-level dysfunction at the individual level, facilitating the transition towards mechanistically targeted treatments.

The role of functional emotion circuits in distinct dimensions of psychopathology in youth.

Several mental disorders emerge during childhood or adolescence and are often characterized by socioemotional difficulties, including alterations in emotion perception. Emotional facial expressions are processed in discrete functional brain modules whose connectivity patterns encode emotion categories, but the involvement of these neural circuits in psychopathology in youth is poorly understood. This study examined the associations between activation and functional connectivity patterns in emotion circuits and psychopathology during development. We used task-based fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC, N = 1221, 8-23 years) and conducted generalized psycho-physiological interaction (gPPI) analyses. Measures of psychopathology were derived from an independent component analysis of questionnaire data. The results showed positive associations between identifying fearful, sad, and angry faces and depressive symptoms, and a negative relationship between sadness recognition and positive psychosis symptoms. We found a positive main effect of depressive symptoms on BOLD activation in regions overlapping with the default mode network, while individuals reporting higher levels of norm-violating behavior exhibited emotion-specific lower functional connectivity within regions of the salience network and between modules that overlapped with the salience and default mode network. Our findings illustrate the relevance of functional connectivity patterns underlying emotion processing for behavioral problems in children and adolescents.

Dynamicity of brain network organization & their community architecture as characterizing features for classification of common mental disorders from whole-brain connectome.

The urgency of addressing common mental disorders (bipolar disorder, attention-deficit hyperactivity disorder (ADHD), and schizophrenia) arises from their significant societal impact. Developing strategies to support psychiatrists is crucial. Previous studies focused on the relationship between these disorders and changes in the resting-state functional connectome's modularity, often using static functional connectivity (sFC) estimation. However, understanding the dynamic reconfiguration of resting-state brain networks with rich temporal structure is essential for comprehending neural activity and addressing mental health disorders. This study proposes an unsupervised approach combining spatial and temporal characterization of brain networks to classify common mental disorders using fMRI timeseries data from two cohorts (N = 408 participants). We employ the weighted stochastic block model to uncover mesoscale community architecture differences, providing insights into network organization. Our approach overcomes sFC limitations and biases in community detection algorithms by modelling the functional connectome's temporal dynamics as a landscape, quantifying temporal stability at whole-brain and network levels. Findings reveal individuals with schizophrenia exhibit less assortative community structure and participate in multiple motif classes, indicating less specialized network organization. Patients with schizophrenia and ADHD demonstrate significantly reduced temporal stability compared to healthy controls. This study offers insights into functional connectivity (FC) patterns' spatiotemporal organization and their alterations in common mental disorders, highlighting the potential of temporal stability as a biomarker.

Cerebral glucose metabolic correlates of cognitive and behavioural impairments in amyotrophic lateral sclerosis.

OBJECTIVE: Half of ALS patients are cognitively and/or behaviourally impaired. As cognition/behaviour and cerebral glucose metabolism can be correlated by means of 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET), we aimed to utilise FDG-PET, first, to replicate group-level differences in glucose metabolism between non-demented ALS patients separated into non-impaired (ALSni), cognitively impaired (ALSci), behaviourally impaired (ALSbi), and cognitively and behaviourally impaired (ALScbi) groups; second, to investigate glucose metabolism and performance in various cognitive domains; and third, to examine the impact of partial volume effects correction (PVEC) of the FDG-PET data on the results. METHODS: We analysed neuropsychological, clinical, and imaging data from 67 ALS patients (30 ALSni, 21 ALSci, 5 ALSbi, and 11 ALScbi). Cognition was assessed with the Edinburgh Cognitive and Behavioural ALS Screen, and two social cognition tests. FDG-PET and structural MRI scans were acquired for each patient. Voxel-based statistical analyses were undertaken on grey matter volume (GMV) and non-corrected vs. PVE-corrected FDG-PET scans. RESULTS: ALSci and ALScbi had lower cognitive scores than ALSni. In contrast to both ALSni and ALSci, ALScbi showed widespread hypometabolism in the superior- and middle-frontal gyri in addition to the right temporal pole. Correlations were observed between the GMV, the FDG-PET signal, and various cognitive scores. The FDG-PET results were largely unaffected by PVEC. INTERPRETATION: Our study identified widespread differences in hypometabolism in the ALScbi-ni but not in the ALSci-ni group comparison, raising the possibility that cerebral metabolism may be more closely related to the presence of behavioural changes than to mild cognitive deficits.