Neuroimaging signature of neuropsychiatric disorders

Compressed cerebellar functional connectome hierarchy in spinocerebellar ataxia type 3

Spinocerebellar ataxia type 3 (SCA3) is an inherited movement disorder characterized by a progressive decline in motor coordination. Despite the extensive functional connectivity (FC) alterations reported in previous SCA3 studies in the cerebellum and cerebellar-cerebral pathways, the influence of these FC disturbances on the hierarchical organization of cerebellar functional regions remains unclear. Here, we compared 35 SCA3 patients with 48 age- and sex-matched healthy controls using a combination of voxel-based morphometry and resting-state functional magnetic resonance imaging to investigate whether cerebellar hierarchical organization is altered in SCA3. Utilizing connectome gradients, we identified the gradient axis of cerebellar hierarchical organization, spanning sensorimotor to transmodal (task-unfocused) regions. Compared to healthy controls, SCA3 patients showed a compressed hierarchical organization in the cerebellum at both voxel-level (p < .05, TFCE corrected) and network-level (p < .05, FDR corrected). This pattern was observed in both intra-cerebellar and cerebellar-cerebral gradients. We observed that decreased intra-cerebellar gradient scores in bilateral Crus I/II both negatively correlated with SARA scores (left/right Crus I/II: r = -.48/-.50, p = .04/.04, FDR corrected), while increased cerebellar-cerebral gradients scores in the vermis showed a positive correlation with disease duration (r = .48, p = .04, FDR corrected). Control analyses of cerebellar gray matter atrophy revealed that gradient alterations were associated with cerebellar volume loss. Further FC analysis showed increased functional connectivity in both unimodal and transmodal areas, potentially supporting the disrupted cerebellar functional hierarchy uncovered by the gradients. Our findings provide novel evidence regarding alterations in the cerebellar functional hierarchy in SCA3.

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level.

Clinical trial registration

[clinicaltrials.gov], identifier [NCT03241732].

Exploring Network Properties Across Preclinical Stages of Alzheimer’s Disease Using a Visual Short-Term Memory and Attention Task with High-Density Electroencephalography: A Brain-Connectome Neurophysiological Study

Background: Visual short-term memory (VSTMT) and visual attention (VAT) exhibit decline in the Alzheimer’s disease (AD) continuum; however, network disruption in preclinical stages is scarcely explored. Objective: To advance our knowledge about brain networks in AD and discover connectivity alterations during VSTMT and VAT. Methods: Twelve participants with AD, 23 with mild cognitive impairment (MCI), 17 with subjective cognitive decline (SCD), and 21 healthy controls (HC) were examined using a neuropsychological battery at baseline and follow-up (three years). At baseline, the subjects were examined using high density electroencephalography while performing a VSTMT and VAT. For exploring network organization, we constructed weighted undirected networks and examined clustering coefficient, strength, and betweenness centrality from occipito-parietal regions. Results: One-way ANOVA and pair-wise t-test comparisons showed statistically significant differences in HC compared to SCD (t (36) = 2.43, p = 0.026), MCI (t (42) = 2.34, p = 0.024), and AD group (t (31) = 3.58, p = 0.001) in Clustering Coefficient. Also with regards to Strength, higher values for HC compared to SCD (t (36) = 2.45, p = 0.019), MCI (t (42) = 2.41, p = 0.020), and AD group (t (31) = 3.58, p = 0.001) were found. Follow-up neuropsychological assessment revealed converge of 65% of the SCD group to MCI. Moreover, SCD who were converted to MCI showed significant lower values in all network metrics compared to the SCD that remained stable. Conclusion: The present findings reveal that SCD exhibits network disorganization during visual encoding and retrieval with intermediate values between MCI and HC.

Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored.

METHODS

An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD.

RESULTS

Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations.

CONCLUSION

These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.

Microglial Density Alters Measures of Axonal Integrity and Structural Connectivity

Diffusion tensor imaging (DTI) has fundamentally transformed how we interrogate diseases and disorders of the brain in neuropsychiatric illness. DTI and recently developed multicompartment diffusion-weighted imaging (MC-DWI) techniques, such as NODDI (neurite orientation dispersion and density imaging), measure diffusion anisotropy presuming a static neuroglial environment; however, microglial morphology and density are highly dynamic in psychiatric illness, and how alterations in microglial density might influence intracellular measures of diffusion anisotropy in DTI and MC-DWI brain microstructure is unknown. To address this question, DTI and MC-DWI studies of murine brains depleted of microglia were performed, revealing significant alterations in axonal integrity and fiber tractography in DTI and in commonly used MC-DWI models. With accumulating evidence of the role of microglia in neuropsychiatric illness, our findings uncover the unexpected contribution of microglia to measures of axonal integrity and structural connectivity and provide unanticipated insights into the potential influence of microglia in diffusion imaging studies of neuropsychiatric disease.

Mesoscopic Imaging: Shining a Wide Light on Large-Scale Neural Dynamics

Optical imaging has revolutionized our ability to monitor brain activity, spanning spatial scales from synapses to cells to circuits. Here, we summarize the rapid development and application of mesoscopic imaging, a widefield fluorescence-based approach that balances high spatiotemporal resolution with extraordinarily large fields of view. By leveraging the continued expansion of fluorescent reporters for neuronal activity and novel strategies for indicator expression, mesoscopic analysis enables measurement and correlation of network dynamics with behavioral state and task performance. Moreover, the combination of widefield imaging with cellular resolution methods such as two-photon microscopy and electrophysiology is bridging boundaries between cellular and network analyses. Overall, mesoscopic imaging provides a powerful option in the optical toolbox for investigation of brain function.

A Brain Network Constructed on an L1-Norm Regression Model Is More Sensitive in Detecting Small World Network Changes in Early AD

Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-world attributes of the brain networks of three groups of patients, namely, those with mild cognitive impairment (MCI), Alzheimer's disease (AD), and healthy controls (HCs); we attempted to identify the method that may detect minor differences in MCI and AD patients. Twenty-four AD patients, 33 MCI patients, and 27 HC elderly subjects were subjected to functional MRI (fMRI). We applied traditional Pearson correlation and the L1-norm to construct the brain networks and then tested the small-world attributes by calculating the following parameters: clustering coefficient (Cp), path length (Lp), global efficiency (Eg), and local efficiency (Eloc). As expected, L1 could detect slight changes, mainly in MCI patients expressing higher Cp and Eloc; however, no statistical differences were found between MCI patients and HCs in terms of Cp, Lp, Eg, and Eloc, using Pearson correlation. Compared with HCs, AD patients expressed a lower Cp, Eloc, and Lp and an increased Eg using both connectivity metrics. The statistical differences between the groups indicated the brain networks constructed by the L1-norm were more sensitive to detect slight small-world network changes in early stages of AD.

Convergent brain microstructure across multiple genetic models of schizophrenia and autism spectrum disorder: A feasibility study

Neuroimaging studies of psychiatric illness have revealed a broad spectrum of structural and functional perturbations that have been attributed in part to the complex genetic heterogeneity underpinning these disorders. These perturbations have been identified in both preclinical genetic models and in patients when compared to control populations, but recent work has also demonstrated strong evidence for genetic, molecular, and structural convergence of several psychiatric diseases. We explored potential similarities in neural microstructure in preclinical genetic models of ASD (Fmr1, Nrxn1, Pten) and schizophrenia (Disc1 svΔ2) and in age- and sex-matched control animals with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Our findings demonstrate a convergence in brain microstructure across these four genetic models with both tract-based and region-of-interest based analyses, which continues to buttress an emerging understanding of converging neural microstructure in psychiatric disease.

A Novel Connectome-Based Electrophysiological Study of Subjective Cognitive Decline Related to Alzheimer's Disease by Using Resting-State High-Density EEG EGI GES 300

Aim: To investigate for the first time the brain network in the Alzheimer’s disease (AD) spectrum by implementing a high-density electroencephalography (HD-EEG - EGI GES 300) study with 256 channels in order to seek if the brain connectome can be effectively used to distinguish cognitive impairment in preclinical stages. Methods: Twenty participants with AD, 30 with mild cognitive impairment (MCI), 20 with subjective cognitive decline (SCD) and 22 healthy controls (HC) were examined with a detailed neuropsychological battery and 10 min resting state HD-EEG. We extracted correlation matrices by using Pearson correlation coefficients for each subject and constructed weighted undirected networks for calculating clustering coefficient (CC), strength (S) and betweenness centrality (BC) at global (256 electrodes) and local levels (29 parietal electrodes). Results: One-way ANOVA presented a statistically significant difference among the four groups at local level in CC [F (3, 88) = 4.76, p = 0.004] and S [F (3, 88) = 4.69, p = 0.004]. However, no statistically significant difference was found at a global level. According to the independent sample t-test, local CC was higher for HC [M (SD) = 0.79 (0.07)] compared with SCD [M (SD) = 0.72 (0.09)]; t (40) = 2.39, p = 0.02, MCI [M (SD) = 0.71 (0.09)]; t (50) = 0.41, p = 0.004 and AD [M (SD) = 0.68 (0.11)]; t (40) = 3.62, p = 0.001 as well, while BC showed an increase at a local level but a decrease at a global level as the disease progresses. These findings provide evidence that disruptions in brain networks in parietal organization may potentially represent a key factor in the ability to distinguish people at early stages of the AD continuum. Conclusions: The above findings reveal a dynamically disrupted network organization of preclinical stages, showing that SCD exhibits network disorganization with intermediate values between MCI and HC. Additionally, these pieces of evidence provide information on the usefulness of the 256 HD-EEG in network construction.

Resting-state network topology and planning ability in healthy adults

Functional magnetic resonance imaging (fMRI) studies have been used extensively to investigate the brain areas that are recruited during the Tower of London (ToL) task. Nevertheless, little research has been devoted to study the neural correlates of the ToL task using a network approach. Here we investigated the association between functional connectivity and network topology during resting-state fMRI and ToL task performance, that was performed outside the scanner. Sixty-two (62) healthy subjects (21-74 years) underwent eyes-closed rsfMRI and performed the task on a laptop. We studied global (whole-brain) and within subnetwork resting-state topology as well as functional connectivity between subnetworks, with a focus on the default-mode, fronto-parietal and dorsal and ventral attention networks. Efficiency and clustering coefficient were calculated to measure network integration and segregation, respectively, at both the global and subnetwork level. Our main finding was that higher global efficiency was associated with slower performance (β = 0.22, Pbca = 0.04) and this association seemed mainly driven by inter-individual differences in default-mode network connectivity. The reported results were independent of age, sex, education-level and motion. Although this finding is contrary to earlier findings on general cognition, we tentatively hypothesize that the reported association may indicate that individuals with a more integrated brain during the resting-state are less able to further increase network efficiency when transitioning from a rest to task state, leading to slower responses. This study also adds to a growing body of literature supporting a central role for the default-mode network in individual differences in cognitive performance.

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting‐state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low‐frequency fluctuation, regional homogeneity and two connectome‐wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10‐fold cross‐validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.

Sex-specific deficits in neurite density and white matter integrity are associated with targeted disruption of exon 2 of the Disc1 gene in the rat

Diffusion tensor imaging (DTI) has provided remarkable insight into our understanding of white matter microstructure and brain connectivity across a broad spectrum of psychiatric disease. While DTI and other diffusion weighted magnetic resonance imaging (MRI) methods have clarified the axonal contribution to the disconnectivity seen in numerous psychiatric diseases, absent from these studies are quantitative indices of neurite density and orientation that are especially important features in regions of high synaptic density that would capture the synaptic contribution to the psychiatric disease state. Here we report the application of neurite orientation dispersion and density imaging (NODDI), an emerging microstructure imaging technique, to a novel Disc1 svΔ2 rat model of psychiatric illness and demonstrate the complementary and more specific indices of tissue microstructure found in NODDI than those reported by DTI. Our results demonstrate global and sex-specific changes in white matter microstructural integrity and deficits in neurite density as a consequence of the Disc1 svΔ2 genetic variation and highlight the application of NODDI and quantitative measures of neurite density and neurite dispersion in psychiatric disease.

Aripiprazole Selectively Reduces Motor Tics in a Young Animal Model for Tourette's Syndrome and Comorbid Attention Deficit and Hyperactivity Disorder

Tourette’s syndrome (TS) is a neurodevelopmental disorder characterized primarily by motor and vocal tics. Comorbidities such as attention deficit and hyperactivity disorder (ADHD) are observed in over 50% of TS patients. We applied aripiprazole in a juvenile rat model that displays motor tics and hyperactivity. We additionally assessed the amount of ultrasonic vocalizations (USVs) as an indicator for the presence of vocal tics and evaluated the changes in the striatal neurometabolism using in vivo proton magnetic resonance spectroscopy (1H-MRS) at 11.7T. Thirty-one juvenile spontaneously hypertensive rats (SHRs) underwent bicuculline striatal microinjection and treatment with either aripiprazole or vehicle. Control groups were sham operated and sham injected. Behavior, USVs, and striatal neurochemical profile were analyzed at early, middle, and late adolescence (postnatal days 35 to 50). Bicuculline microinjections in the dorsolateral striatum induced motor tics in SHR juvenile rats. Acute aripiprazole administration selectively reduced both tic frequency and latency, whereas stereotypies, USVs, and hyperactivity remained unaltered. The striatal neurochemical profile was only moderately altered after tic-induction and was not affected by systemic drug treatment. When applied to a young rat model that provides high degrees of construct, face, and predictive validity for TS and comorbid ADHD, aripiprazole selectively reduces motor tics, revealing that tics and stereotypies are distinct phenomena in line with clinical treatment of patients. Finally, our 1H-MRS results suggest a critical revision of the striatal role in the hypothesized cortico-striatal dysregulation in TS pathophysiology.

Tourette syndrome and other neurodevelopmental disorders: a comprehensive review

Gilles de la Tourette syndrome (TS) is a complex developmental neuropsychiatric condition in which motor manifestations are often accompanied by comorbid conditions that impact the patient's quality of life. In the DSM-5, TS belongs to the "neurodevelopmental disorders" group, together with other neurodevelopmental conditions, frequently co-occurring. In this study, we searched the PubMed database using a combination of keywords associating TS and all neurodevelopmental diagnoses. From 1009 original reports, we identified 36 studies addressing TS and neurodevelopmental comorbidities. The available evidence suggests the following: (1) neurodevelopmental comorbidities in TS are the rule, rather than the exception; (2) attention deficit/hyperactivity disorder (ADHD) is the most frequent; (3) there is a continuum from a simple (TS + ADHD or/and learning disorder) to a more complex phenotype (TS + autism spectrum disorder). We conclude that a prompt diagnosis and a detailed description of TS comorbidities are necessary not only to understand the aetiological basis of neurodevelopmental disorders but also to address specific rehabilitative and therapeutic approaches.

Anterior pallidal deep brain stimulation for Tourette's syndrome: a randomised, double-blind, controlled trial

BACKGROUND

Deep brain stimulation (DBS) has been proposed to treat patients with severe Tourette's syndrome, and open-label trials and two small double-blind trials have tested DBS of the posterior and the anterior internal globus pallidus (aGPi). We aimed to specifically assess the efficacy of aGPi DBS for severe Tourette's syndrome.

METHODS

In this randomised, double-blind, controlled trial, we recruited patients aged 18-60 years with severe and medically refractory Tourette's syndrome from eight hospitals specialised in movement disorders in France. Enrolled patients received surgery to implant bilateral electrodes for aGPi DBS; 3 months later they were randomly assigned (1:1 ratio with a block size of eight; computer-generated pairwise randomisation according to order of enrolment) to receive either active or sham stimulation for the subsequent 3 months in a double-blind fashion. All patients then received open-label active stimulation for the subsequent 6 months. Patients and clinicians assessing outcomes were masked to treatment allocation; an unmasked clinician was responsible for stimulation parameter programming, with intensity set below the side-effect threshold. The primary endpoint was difference in Yale Global Tic Severity Scale (YGTSS) score between the beginning and end of the 3 month double-blind period, as assessed with a Mann-Whitney-Wilcoxon test in all randomly allocated patients who received active or sham stimulation during the double-blind period. We assessed safety in all patients who were enrolled and received surgery for aGPi DBS. This trial is registered with ClinicalTrials.gov, number NCT00478842.

FINDINGS

Between Dec 6, 2007, and Dec 13, 2012, we enrolled 19 patients. We randomly assigned 17 (89%) patients, with 16 completing blinded assessments (seven [44%] in the active stimulation group and nine [56%] in the sham stimulation group). We noted no significant difference in YGTSS score change between the beginning and the end of the 3 month double-blind period between groups (active group median YGTSS score 68·5 [IQR 34·0 to 83·5] at the beginning and 62·5 [51·5 to 72·0] at the end, median change 1·1% [IQR -23·9 to 38·1]; sham group 73·0 [69·0 to 79·0] and 79·0 [59·0 to 81·5], median change 0·0% [-10·6 to 4·8]; p=0·39). 15 serious adverse events (three in patients who withdrew before stimulation and six each in the active and sham stimulation groups) occurred in 13 patients (three who withdrew before randomisation, four in the active group, and six in the sham group), with infections in DBS hardware in four patients (two who withdrew before randomisation, one in the sham stimulation group, and one in the active stimulation group). Other serious adverse events included one electrode misplacement (active stimulation group), one episode of depressive signs (active stimulation group), and three episodes of increased tic severity and anxiety (two in the sham stimulation group and one in the active stimulation group).

INTERPRETATION

3 months of aGPi DBS is insufficient to decrease tic severity for patients with Tourette's syndrome. Future research is needed to investigate the efficacy of aGPi DBS for patients over longer periods with optimal stimulation parameters and to identify potential predictors of the therapeutic response.

FUNDING

French Ministry of Health.

Small-world human brain networks: Perspectives and challenges

Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.

Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data

High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal's quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data.

The anarchic brain in action: the contribution of task-based fMRI studies to the understanding of Gilles de la Tourette syndrome

PURPOSE OF REVIEW

Gilles de la Tourette syndrome (GTS) is a frequent neurological disorder characterized by the production of tics, and frequently associated with obsessive-compulsive disorder or attention-deficit hyperactivity disorder. The aim of this article is to summarize the contribution of imaging activation techniques to the study of the syndrome.

RECENT FINDINGS

GTS has been studied with a variety of functional MRI (fMRI)/PET activation paradigms to characterize the origin of tics or their suppression, and how they compare physiologically with voluntary actions or response inhibitions. Current studies indicate overactivations of prefrontal and premotor cortices, including the supplementary motor area, and subcortical structures. Resting state functional connectivity studies complement activation studies in showing perturbed connectivity of cortico-subcortical networks. Several such findings correlate with the severity of the disease.

SUMMARY

fMRI activation techniques are contributing a system-level neurophysiological description of GTS and bridge the gap between animal models and clinical observations. fMRI clarifies brain networks involved in different aspects of GTS phenomenology with some good clinical face validity. A future generation of fMRI studies should have higher ambitions and contribute, for example, to treatment optimization including the identification of ideal targets for deep brain stimulation in drug-resistant cases; however, such goals will be achieved only through controlled large-scale cooperative studies.

Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach

Bipolar disorder is often misdiagnosed as major depressive disorder, which leads to inadequate treatment. Depressed individuals versus healthy control subjects, show increased expectation of negative outcomes. Due to increased impulsivity and risk for mania, however, depressed individuals with bipolar disorder may differ from those with major depressive disorder in neural mechanisms underlying anticipation processes. Graph theory methods for neuroimaging data analysis allow the identification of connectivity between multiple brain regions without prior model specification, and may help to identify neurobiological markers differentiating these disorders, thereby facilitating development of better therapeutic interventions. This study aimed to compare brain connectivity among regions involved in win/loss anticipation in depressed individuals with bipolar disorder (BDD) versus depressed individuals with major depressive disorder (MDD) versus healthy control subjects using graph theory methods. The study was conducted at the University of Pittsburgh Medical Center and included 31 BDD, 39 MDD, and 36 healthy control subjects. Participants were scanned while performing a number guessing reward task that included the periods of win and loss anticipation. We first identified the anticipatory network across all 106 participants by contrasting brain activation during all anticipation periods (win anticipation + loss anticipation) versus baseline, and win anticipation versus loss anticipation. Brain connectivity within the identified network was determined using the Independent Multiple sample Greedy Equivalence Search (IMaGES) and Linear non-Gaussian Orientation, Fixed Structure (LOFS) algorithms. Density of connections (the number of connections in the network), path length, and the global connectivity direction ('top-down' versus 'bottom-up') were compared across groups (BDD/MDD/healthy control subjects) and conditions (win/loss anticipation). These analyses showed that loss anticipation was characterized by denser top-down fronto-striatal and fronto-parietal connectivity in healthy control subjects, by bottom-up striatal-frontal connectivity in MDD, and by sparse connectivity lacking fronto-striatal connections in BDD. Win anticipation was characterized by dense connectivity of medial frontal with striatal and lateral frontal cortical regions in BDD, by sparser bottom-up striatum-medial frontal cortex connectivity in MDD, and by sparse connectivity in healthy control subjects. In summary, this is the first study to demonstrate that BDD and MDD with comparable levels of current depression differed from each other and healthy control subjects in density of connections, connectivity path length, and connectivity direction as a function of win or loss anticipation. These findings suggest that different neurobiological mechanisms may underlie aberrant anticipation processes in BDD and MDD, and that distinct therapeutic strategies may be required for these individuals to improve coping strategies during expectation of positive and negative outcomes.