Opportunities and Challenges for Psychiatry in the Connectomic Era

The mesolimbic system and the loss of higher order network features in schizophrenia when learning without reward

Introduction

Schizophrenia is characterized by a loss of network features between cognition and reward sub-circuits (notably involving the mesolimbic system), and this loss may explain deficits in learning and cognition. Learning in schizophrenia has typically been studied with tasks that include reward related contingencies, but recent theoretical models have argued that a loss of network features should be seen even when learning without reward. We tested this model using a learning paradigm that required participants to learn without reward or feedback. We used a novel method for capturing higher order network features, to demonstrate that the mesolimbic system is heavily implicated in the loss of network features in schizophrenia, even when learning without reward.

Methods

fMRI data (Siemens Verio 3T) were acquired in a group of schizophrenia patients and controls (n=78; 46 SCZ, 18 ≤ Age ≤ 50) while participants engaged in associative learning without reward-related contingencies. The task was divided into task-active conditions for encoding (of associations) and cued-retrieval (where the cue was to be used to retrieve the associated memoranda). No feedback was provided during retrieval. From the fMRI time series data, network features were defined as follows: First, for each condition of the task, we estimated 2nd order undirected functional connectivity for each participant (uFC, based on zero lag correlations between all pairs of regions). These conventional 2nd order features represent the task/condition evoked synchronization of activity between pairs of brain regions. Next, in each of the patient and control groups, the statistical relationship between all possible pairs of 2nd order features were computed. These higher order features represent the consistency between all possible pairs of 2nd order features in that group and embed within them the contributions of individual regions to such group structure.

Results

From the identified inter-group differences (SCZ ≠ HC) in higher order features, we quantified the respective contributions of individual brain regions. Two principal effects emerged: 1) SCZ were characterized by a massive loss of higher order features during multiple task conditions (encoding and retrieval of associations). 2) Nodes in the mesolimbic system were over-represented in the loss of higher order features in SCZ, and notably so during retrieval.

Discussion

Our analytical goals were linked to a recent circuit-based integrative model which argued that synergy between learning and reward circuits is lost in schizophrenia. The model's notable prediction was that such a loss would be observed even when patients learned without reward. Our results provide substantial support for these predictions where we observed a loss of network features between the brain's sub-circuits for a) learning (including the hippocampus and prefrontal cortex) and b) reward processing (specifically constituents of the mesolimbic system that included the ventral tegmental area and the nucleus accumbens. Our findings motivate a renewed appraisal of the relationship between reward and cognition in schizophrenia and we discuss their relevance for putative behavioral interventions.

Intra-individual cortical networks in Anorexia Nervosa: Evidence from a longitudinal dataset

OBJECTIVE

This work investigates cortical thickness (CT) and gyrification patterns in Anorexia Nervosa (AN) before and after short-term weight restoration using graph theory tools.

METHODS

38 female adolescents with AN underwent structural magnetic resonance imaging scans at baseline and after - on average - 3.5 months following short-term weight restoration while 53 age-matched healthy controls (HCs) were scanned once. Graph measures were compared between groups and longitudinally within the AN group. Associations with clinical measures such as age of onset, duration of illness, BMI standard deviation score (BMI-SDS), and longitudinal weight changes were tested via stepwise regression.

RESULTS

Cortical thickness graphs of patients with acute AN displayed lower modularity and small-world index (SWI) than HCs. Modularity recovered after weight gain. Reduced global efficiency and SWI were observed in patients at baseline compared to HCs based on gyrification networks. Significant associations between local clustering of CT at admission and BMI-SDS, and clustering/global efficiency of gyrification and duration of illness emerged.

CONCLUSIONS

Our results indicate a shift towards less organised CT networks in patients with acute AN. After weight recovery, the disarrangement seems to be partially reduced. However, longer-term follow-ups are needed to determine whether cortical organizational patterns fully return to normal.

Exploring the role of hub and network dysfunction in brain connectomes of schizophrenia using functional magnetic resonance imaging

Introduction

Pathophysiological etiology of schizophrenia remains unclear due to the heterogeneous nature of its biological and clinical manifestations. Dysfunctional communication among large-scale brain networks and hub nodes have been reported. In this study, an exploratory approach was adopted to evaluate the dysfunctional connectome of brain in schizophrenia.

Methods

Two hundred adult individuals with schizophrenia and 200 healthy controls were recruited from Taipei Veterans General Hospital. All subjects received functional magnetic resonance imaging (fMRI) scanning. Functional connectivity (FC) between parcellated brain regions were obtained. Pair-wise brain regions with significantly different functional connectivity among the two groups were identified and further analyzed for their concurrent ratio of connectomic differences with another solitary brain region (single-FC dysfunction) or dynamically interconnected brain network (network-FC dysfunction).

Results

The right thalamus had the highest number of significantly different pair-wise functional connectivity between schizophrenia and control groups, followed by the left thalamus and the right middle frontal gyrus. For individual brain regions, dysfunctional single-FCs and network-FCs could be found concurrently. Dysfunctional single-FCs distributed extensively in the whole brain of schizophrenia patients, but overlapped in similar groups of brain nodes. A dysfunctional module could be formed, with thalamus being the key dysfunctional hub.

Discussion

The thalamus can be a critical hub in the brain that its dysfunctional connectome with other brain regions is significant in schizophrenia patients. Interconnections between dysfunctional FCs for individual brain regions may provide future guide to identify critical brain pathology associated with schizophrenia.

Altered language network lateralization in euthymic bipolar patients: a pilot study

Bipolar patients (BD) in the euthymic phase show almost no symptoms, nevertheless possibility of relapse is still present. We expected to find a psychobiological trace of their vulnerability by analyzing a specific network-the Language Network (LN)-connecting many high-level processes and brain regions measured at rest. According to Crow's hypothesis on the key role of language in the origin of psychoses, we expected an altered asymmetry of the LN in euthymic BDs. Eighteen euthymic BD patients (10 females; age = 54.50 ± 11.38 years) and 16 healthy controls (HC) (8 females; age = 51.16 ± 11.44 years) underwent a functional magnetic resonance imaging scan at rest. The LN was extracted through independent component analysis. Then, LN time series was used to compute the fractional amplitude of the low-frequency fluctuation (fALFF) index, which was then correlated with clinical scales. Compared with HC, euthymic patients showed an altered LN with greater activation of Broca's area right homologous and anterior insula together with reduced activation of left middle temporal gyrus. The normalized fALFF analysis on BD patients' LN time series revealed that the Slow-5 fALFF band was positively correlated with residual mania symptoms but negatively associated with depression scores. In line with Crow's hypothesis postulating an altered language hemispheric asymmetry in psychoses, we revealed, in euthymic BD patients, a right shift involving both the temporal and frontal linguistic hubs. The fALFF applied to LN allowed us to highlight a number of significant correlations of this measure with residual mania and depression psychiatric symptoms.

Schizotypal traits across the amyotrophic lateral sclerosis-frontotemporal dementia spectrum: pathomechanistic insights

BACKGROUND

Psychiatric presentations similar to that observed in primary psychiatric disorders are well described across the amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) spectrum. Despite this, schizotypal personality traits associated with increased risks of clinical psychosis development and poor psychosocial outcomes have never been examined. The current study aimed to provide the first exploration of schizotypal traits and its neural underpinnings in the ALS-FTD spectrum to gain insights into a broader spectrum of psychiatric overlap with psychiatric disorders.

METHODS

Schizotypal traits were assessed using the targeted Schizotypal Personality Questionnaire in 99 participants (35 behavioural variant FTD, 10 ALS-FTD and 37 ALS patients, and 17 age-, sex- and education-matched healthy controls). Voxel-based morphometry analysis of whole-brain grey matter volume was conducted.

RESULTS

Relative to controls, pervasive schizotypal personality traits across positive and negative schizotypy and disorganised thought disorders were identified in behavioural variant FTD, ALS (with the exception of negative schizotypy) and ALS-FTDALS-FTD patients (all p < .013), suggesting the presence of a wide spectrum of subclinical schizotypal symptoms beyond classic psychotic symptoms. Atrophy in frontal, anterior cingulate and insular cortices, and caudate and thalamus was involved in positive schizotypy, while integrity of the cerebellum was associated with disorganised thought disorder traits.

CONCLUSIONS

The frontal-striatal-limbic regions underpinning manifestation of schizotypy in the ALS-FTDALS-FTD spectrum are similar to that established in previous schizophrenia research. This finding expands the concept of a psychiatric overlap in ALS-FTD and schizophrenia, and suggests potentially common underlying mechanisms involving disruptions to frontal-striatal-limbic networks, warranting a transdiagnostic approach for future investigations.

Structural and Functional MRI Brain Changes in Patients with Schizophrenia Following Electroconvulsive Therapy: A Systematic Review

BACKGROUND

Schizophrenia (SZ) is a severe psychiatric disorder typically characterized by multidimensional psychotic syndromes. Electroconvulsive therapy (ECT) is a treatment option for medication-resistant patients with SZ or treating acute symptoms. Although the efficacy of ECT has been demonstrated in clinical use, its therapeutic mechanisms in the brain remain elusive.

OBJECTIVE

This study aimed to summarize brain changes on structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) after ECT.

METHODS

According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was carried out. The PubMed and Medline databases were systematically searched using the following medical subject headings (MeSH): (electroconvulsive therapy OR ECT) AND (schizophrenia) AND (MRI OR fMRI OR DTI OR DWI).

RESULTS

This review yielded 12 MRI studies, including 4 with sMRI, 5 with fMRI and 3 with multimodal MRI. Increases in volumes of the hippocampus and its adjacent regions (parahippocampal gyrus and amygdala), as well as the insula and frontotemporal regions, were noted after ECT. fMRI studies found ECT-induced changes in different brain regions/networks, including the hippocampus, amygdala, default model network, salience network and other regions/networks that are thought to highly correlate with the pathophysiologic characteristics of SZ. The results of the correlation between brain changes and symptom remissions are inconsistent.

CONCLUSION

Our review provides evidence supporting ECT-induced brain changes on sMRI and fMRI in SZ and explores the relationship between these changes and symptom remission.

Disassociated and concurrent structural and functional abnormalities in the drug-naïve first-episode early onset schizophrenia

Schizophrenia which is an abnormally developmental disease has been widely reported to show abnormal brain structure and function. Enhanced functional integration is a predominant neural marker for brain mature. Abnormal development of structure and functional integration may be a biomarker for early diagnosis of schizophrenia. Fifty-five patients with early onset schizophrenia (EOS) and 79 healthy controls were enrolled in this study. Voxel-based morphometry (VBM) and functional connectivity density (FCD) were performed to explore gray matter volume (GMV) lesion, abnormal functional integration, and concurrent structural and functional abnormalities in the brain. Furthermore, the relationships between abnormalities structural and function and clinical characteristics were evaluated in EOS. Compared with healthy controls, EOS showed significantly decreased GMV in the bilateral OFC, frontal, temporal, occipital, parietal and limbic system. EOS also showed decreased FCD in precuneus and increased FCD in cerebellum. Moreover, we found concurrent changes of structure and function in left lateral orbitofrontal cortex (lOFC). Finally, correlation analyses did not find significant correlation between abnormal neural measurements and clinical characteristic in EOS. The results reveal disassociated and bound structural and functional abnormalities patterns in EOS suggesting structural and functional measurements play different roles in delineating the abnormal patterns of EOS. The concurrent structural and functional changes in lOFC may be a biomarker for early diagnosis of schizophrenia. Our findings will deepen our understanding of the pathophysiological mechanisms in EOS.

Enduring effect of abuse: Childhood maltreatment links to altered theory of mind network among adults

Childhood maltreatment (CM) confers a great risk of maladaptive development outcomes later in life, however, the neurobiological mechanism underlying this vulnerability is still unclear. The present study aimed to investigate the long-term consequences of CM on neural connectivity while controlling for psychiatric conditions, medication, and, substance abuse. A sample including adults with (n = 40) and without CM (n = 50) completed Childhood Trauma Questionnaire (CTQ), personality questionnaires, and resting-state functional magnetic resonance imaging scan were recruited for the current study. The whole-brain functional connectivity (FC) was evaluated using an unbiased, data-driven, multivariate pattern analysis method. Relative to controls, adults with CM suffered a higher level of temperament and impulsivity and showed decreased FC between the insula and superior temporal gyrus (STG) and between inferior parietal lobule (IPL) and middle frontal gyrus, STG, and dorsal anterior cingulate cortex (dACC), while increased FC between IPL and cuneus and superior frontal gyrus (SFG) regions. The FCs of IPL with dACC and SFG were correlated with the anxious and cyclothymic temperament and attentional impulsivity. Moreover, these FCs partially mediated the relationship between CM and attentional impulsivity. Our results suggest that CM has a significant effect on the modulation of FC within theory of mind (ToM) network even decades later in adulthood, and inform a new framework to account for how CM results in the development of impulsivity. The novel findings reveal the neurobiological consequences of CM and provide new clues to the prevention and intervention strategy to reduce the risk of the development of psychopathology.

Shift Toward Randomness in Brain Networks of Patients With Anorexia Nervosa: The Role of Malnutrition

No study to date investigated structural white matter (WM) connectome characteristics in patients with anorexia nervosa (AN). Previous research in AN found evidence of imbalances in global and regional connectomic brain architecture and highlighted a role of malnutrition in determining structural brain changes. The aim of our study was to explore the characteristics of the WM network architecture in a sample of patients with AN. Thirty-six patients with AN and 36 healthy women underwent magnetic resonance imaging to obtain a high-resolution three-dimensional T1-weighted anatomical image and a diffusion tensor imaging scan. Probabilistic tractography data were extracted and analyzed in their network properties through graph theory tools. In comparison to healthy women, patients with AN showed lower global network segregation (normalized clustering: p = 0.029), an imbalance between global network integration and segregation (i.e., lower small-worldness: p = 0.031), and the loss of some of the most integrative and influential hubs. Both clustering and small-worldness correlated with the lowest lifetime body mass index. A significant relationship was found between the average regional loss of cortical volume and changes in network properties of brain nodes: the more the difference in the cortical volume of brain areas, the more the increase in the centrality of corresponding nodes in the whole brain, and the decrease in clustering and efficiency of the nodes of parietal cortex. Our findings showed an unbalanced connectome wiring in AN patients, which seems to be influenced by malnutrition and loss of cortical volume. The role of this rearrangement in the maintenance and prognosis of AN and its reversibility with clinical improvement needs to be established by future studies.

Borderline personality disorder classification based on brain network measures during emotion regulation

Borderline Personality Disorder (BPD) is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the critical neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study, we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Patients with borderline personality disorder (n = 51), cluster C personality disorder (n = 26) and non-patient controls (n = 44), performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task-related connectivity: phasic refers to task-event dependent changes in connectivity, while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency, and participation coefficient) and entered as separate models in a nested cross-validated linear support vector machine classification analysis. Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p = 0.23. Exploratory analyses did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analysis. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD.

White matter microstructure and connectivity in patients with obsessive-compulsive disorder and their unaffected siblings

OBJECTIVE

We aimed to examine white matter microstructure and connectivity in individuals with obsessive-compulsive disorder (OCD) and their unaffected siblings, relative to healthy controls.

METHODS

Diffusion-weighted magnetic resonance imaging (dMRI) scans were acquired in 30 patients with OCD, 21 unaffected siblings, and 31 controls. We examined white matter microstructure using measures of fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD). Structural networks were examined using network-based statistic (NBS).

RESULTS

Compared to controls, OCD patients showed significantly reduced FA and increased RD in clusters traversing the left forceps minor, inferior fronto-occipital fasciculus, anterior thalamic radiation, and cingulum. Furthermore, the OCD group displayed significantly weaker connectivity (quantified by the streamline count) compared to controls in the right hemisphere, most notably in edges connecting subcortical structures to temporo-occipital cortical regions. The sibling group showed intermediate streamline counts, FA and RD values between OCD and healthy control groups in connections found to be abnormal in patients with OCD. However, these reductions did not significantly differ compared to controls.

CONCLUSION

Therefore, siblings of OCD patients display intermediate levels in dMRI measures of microstructure and connectivity, suggesting white matter abnormalities might be related to the familial predisposition for OCD.

Temporoparietal Connectivity Within Default Mode Network Associates With Clinical Improvements in Schizophrenia Following Modified Electroconvulsive Therapy

Although modified electroconvulsive therapy (ECT) has been reported to be effective for the treatment of schizophrenia (SCZ), its action mechanism is unclear. To elucidate the underlying ECT mechanisms of SCZ, this study used a longitudinal cohort including 21 SCZ patients receiving only antipsychotics (DSZ group) and 21 SCZ patients receiving a regular course of ECT combining with antipsychotics (MSZ group) for 4 weeks. All patients underwent magnetic resonance imaging (MRI) scans at baseline (t1) and follow-up (t2) time points. A matched healthy control (HC) group included 23 individuals who were only scanned at baseline. Functional connectivity (FC) within the default mode network (DMN) was evaluated before and after ECT. Significant interaction of the group over time was found in FC between angular gyrus (AG) and middle temporal gyrus (MTG). Post-hoc analysis showed a significantly enhanced FC of left AG(AG.L) and right MTG (MTG.R) in the MSZ group relative to the DSZ group. In addition, the right AG (AG.R) showed significantly enhanced FC between MTG.R and left MTG (MTG.L) after ECT in the MSZ group, but no in the DSZ group. In particular, the FCs change in AG.L-MTG.R and AG.R-MTG.R were positively correlated with the Positive and Negative Syndrome Scale (PANSS) negative score reduction. Furthermore, the FC change in AG.L-MTG.R was also positively correlated with the PANSS general psychopathology score reduction. These findings confirmed a potential relationship between ECT inducing hyperconnectivity within DMN and improvements in symptomatology of SCZ, suggesting that ECT controls mental symptoms by regulating the temporoparietal connectivity within DMN.

Bridging global and local topology in whole-brain networks using the network statistic jackknife

Whole-brain network analysis is commonly used to investigate the topology of the brain using a variety of neuroimaging modalities. This approach is notable for its applicability to a large number of domains, such as understanding how brain network organization relates to cognition and behavior and examining disrupted brain network organization in disease. A benefit to this approach is the ability to summarize overall brain network organization with a single metric (e.g., global efficiency). However, important local differences in network structure might exist without any corresponding observable differences in global topology, making a whole-brain analysis strategy unlikely to detect relevant local findings. Conversely, using local network metrics can identify local differences, but are not directly informative of differences in global topology. Here, we propose the network statistic (NS) jackknife framework, a simulated lesioning method that combines the utility of global network analysis strategies with the ability to detect relevant local differences in network structure. We evaluate the NS jackknife framework with a simulation study and an empirical example comparing global efficiency in children with attention-deficit/hyperactivity disorder (ADHD) and typically developing (TD) children. The NS jackknife framework has been implemented in a public, open-source R package, netjack, available at https://cran.r-project.org/package=netjack.

Functional Connectivity Patterns and the Role of 5-HTTLPR Polymorphism on Network Architecture in Female Patients With Anorexia Nervosa

Introduction

Recent neuroimaging studies suggest that anorexia nervosa (AN) symptoms emerge from failures in the relationships between spatially distributed networks that support different cognitive, emotional, and somatosensory functions. The 5-HTTLPR genotype has been shown to modulate all these abilities in AN, as well as the connectivity patterns between brain regions that support their functioning. This study aims at exploring the presence of any difference in functional connectome properties between AN patients and healthy controls (HC) by means of graph theory tools. The effect of 5-HTTLPR genotype on regional and global network characteristics in AN and HC was also explored.

Methods

A sample of 74 subjects (38 HC, 36 AN) underwent a resting state functional magnetic resonance imaging and was genotyped for 5-HTTLPR polymorphism. Comparisons of network properties were made between the AN and HC groups and, within each group, between 5-HTTLPR carriers of low-functioning alleles and carriers of the long–long genotype.

Results

Patients with AN displayed lower network clustering than HC (p = 0.04 at Mann–Whitney U test). Based on both degree and betweenness, a different distribution of network hubs emerged in the two groups. In particular, the anterior part of the anterior cingulate cortex was a hub only in the patient group. A correlation emerged between differences in brain volumes between patients and HC and differences in degree values of basal ganglia, nodes in the insula, and those in the parietal cortex. Carriers of the short allele of the 5-HTTLPR polymorphism were characterized by lower small-world properties (p = 0.027) and modularity (p = 0.031) in the patient group, and a trend toward higher modularity (p = 0.033) and small-world values (p = 0.123) in the HC group.

Discussion

Patients with AN showed differences in hubs distribution, providing evidence of the presence of a different functional architectural backbone in this group. Since some correlation emerged between different degree values of nodes and differences in volumes, further longitudinal studies are warranted to better understand the role of malnutrition on brain network architecture. The opposite effects of 5-HTTLPR polymorphism on global network characteristics in the two groups suggest an interaction of the short allele and malnutrition in modulating brain network properties.

The road ahead in clinical network neuroscience

Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.

Quantifying individual differences in brain morphometry underlying symptom severity in Autism Spectrum Disorders

The neurobiology of heterogeneous neurodevelopmental disorders such as autism spectrum disorders (ASD) are still unclear. Despite extensive efforts, most findings are difficult to reproduce due to high levels of individual variance in phenotypic expression. To quantify individual differences in brain morphometry in ASD, we implemented a novel subject-level, distance-based method on subject-specific attributes. In a large multi-cohort sample, each subject with ASD (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.58) was strictly matched to a control participant (n = 100; n = 84 males; mean age: 11.43 years; mean IQ: 110.70). Intrapair Euclidean distance of MRI brain morphometry and symptom severity measures (Social Responsiveness Scale) were entered into a regularised machine learning pipeline for feature selection, with rigorous out-of-sample validation and permutation testing. Subject-specific structural morphometry features significantly predicted individual variation in ASD symptom severity (19 cortical thickness features, p = 0.01, n = 5000 permutations; 10 surface area features, p = 0.006, n = 5000 permutations). Findings remained robust across subjects and were replicated in validation samples. Identified cortical regions implicate key hubs of the salience and default mode networks as neuroanatomical features of social impairment in ASD. Present results highlight the importance of subject-level markers in ASD, and offer an important step forward in understanding the neurobiology of heterogeneous disorders.

Nonsocial and social cognition in schizophrenia: current evidence and future directions

Cognitive impairment in schizophrenia involves a broad array of nonsocial and social cognitive domains. It is a core feature of the illness, and one with substantial implications for treatment and prognosis. Our understanding of the causes, consequences and interventions for cognitive impairment in schizophrenia has grown substantially in recent years. Here we review a range of topics, including: a) the types of nonsocial cognitive, social cognitive, and perceptual deficits in schizophrenia; b) how deficits in schizophrenia are similar or different from those in other disorders; c) cognitive impairments in the prodromal period and over the lifespan in schizophrenia; d) neuroimaging of the neural substrates of nonsocial and social cognition, and e) relationships of nonsocial and social cognition to functional outcome. The paper also reviews the considerable efforts that have been directed to improve cognitive impairments in schizophrenia through novel psychopharmacology, cognitive remediation, social cognitive training, and alternative approaches. In the final section, we consider areas that are emerging and have the potential to provide future insights, including the interface of motivation and cognition, the influence of childhood adversity, metacognition, the role of neuroinflammation, computational modelling, the application of remote digital technology, and novel methods to evaluate brain network organization. The study of cognitive impairment has provided a way to approach, examine and comprehend a wide range of features of schizophrenia, and it may ultimately affect how we define and diagnose this complex disorder.

Small-world properties of brain morphological characteristics in Anorexia Nervosa

Cortical thickness and gyrification abnormalities in anorexia nervosa (AN) have been recently described, but no attempt has been made to explore their organizational patterns to characterize the neurobiology of the disorder in the different stages of its course. The aim of this study was to explore cortical thickness and gyrification patterns by means of graph theory tools in 38 patients with AN, 20 fully recovered patients, and 38 healthy women (HC). All participants underwent high-resolution magnetic resonance imaging. Connectome properties were compared between: 1) AN patients and HC, 2) fully recovered patients and HC, 3) patients with a full remission at a 3-year follow-up assessment and patients who had not recovered. Small-worldness was greater in patients with acute AN in comparison to HC in both cortical thickness and gyrification networks. In the cortical thickness network, patients with AN also showed increased Local Efficiency, Modularity and Clustering coefficients, whereas integration measures were lower in the same group. Patients with a poor outcome showed higher segregation measures and lower small-worldness in the gyrification network, but no differences emerged for the cortical thickness network. For both cortical thickness and gyrification patterns, regional analyses revealed differences between patients with different outcomes. Different patterns between cortical thickness and gyrification networks are probably due to their peculiar developmental trajectories and sensitivity to environmental influences. The role of gyrification network alterations in predicting the outcome suggests a role of early maturational processes in the prognosis of AN.

Childhood trauma history is linked to abnormal brain connectivity in major depression

Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.

Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging

Early in the course of psychosis, alterations in brain connectivity accompany the emergence of psychiatric symptoms and cognitive impairments, including processing speed. The clinical-staging model is a refined form of diagnosis that places the patient along a continuum of illness conditions, which allows stage-specific interventions with the potential of improving patient care and outcome. This cross-sectional study investigates brain connectivity features that characterize the clinical stages following a first psychotic episode. Structural brain networks were derived from diffusion-weighted MRI for 71 early-psychosis patients and 76 healthy controls. Patients were classified into stage II (first-episode), IIIa (incomplete remission), IIIb (one relapse), and IIIc (two or more relapses), according to the course of the illness until the time of scanning. Brain connectivity measures and diffusion parameters (fractional anisotropy, apparent diffusion coefficient) were investigated using general linear models and sparse linear discriminant analysis (sLDA), studying distinct subgroups of patients who were at specific stages of early psychosis. We found that brain connectivity impairments were more severe in clinical stages following the first-psychosis episode (stages IIIa, IIIb, IIIc) than in first-episode psychosis (stage II) patients. These alterations were spatially diffuse but converged on a set of vulnerable regions, whose inter-connectivity selectively correlated with processing speed in patients and controls. The sLDA suggested that relapsing-remitting (stages IIIb, IIIc) and non-remitting (stage IIIa) patients are characterized by distinct dysconnectivity profiles. Our results indicate that neuroimaging markers of brain dysconnectivity in early psychosis may reflect the heterogeneity of the illness and provide a connectomics signature of the clinical-staging model.

Preschool Executive Function Predicts Childhood Resting-State Functional Connectivity and Attention-Deficit/Hyperactivity Disorder and Depression

BACKGROUND

Measures of executive function (EF), such as the Behavior Rating Inventory of Executive Function, distinguish children with attention-deficit/hyperactivity disorder (ADHD) from control subjects, but less work has examined relationships to depression or brain network organization. This study examined whether early childhood EF predicted new onset or worsening of ADHD and/or depression and examined how early childhood EF related to functional connectivity of brain networks at school age.

METHODS

Participants included 247 children who were enrolled at 3 to 6 years of age from a prospective study of emotion development. The Behavior Rating Inventory of Executive Function Global Executive Composite score was used as the measure of EF in early childhood to predict ADHD and depression diagnoses and symptoms across school age. Resting-state functional magnetic resonance imaging network analyses examined global efficiency in the frontoparietal, cingulo-opercular, salience, and default mode networks and six "hub" seed regions selected to examine between-network connectivity.

RESULTS

Early childhood EF predicted new onset and worsening of ADHD and depression symptoms across school age. Greater EF deficits in preschool predicted increased global efficiency in the salience network and altered connectivity with four regions for the dorsal anterior cingulate cortex hub and one region with the insula hub at school age. This altered connectivity was related to increasing ADHD and depression symptoms.

CONCLUSIONS

Early executive deficits may be an early common liability for risk of developing ADHD and/or depression and were associated with altered functional connectivity in networks and hub regions relevant to executive processes. Future work could help clarify whether specific EF deficits are implicated in the development of both disorders.

Differential patterns of age-related cortical and subcortical functional connectivity in 6-to-10 year old children: A connectome-wide association study

INTRODUCTION

Typical brain development is characterized by specific patterns of maturation of functional networks. Cortico-cortical connectivity generally increases, whereas subcortico-cortical connections often decrease. Little is known about connectivity changes amongst different subcortical regions in typical development.

METHODS

This study examined age- and gender-related differences in functional connectivity between and within cortical and subcortical regions using two different approaches. The participants included 411 six- to ten-year-old typically developing children sampled from the population-based Generation R study. Functional connectomes were defined in native space using regions of interest from subject-specific FreeSurfer segmentations. Connections were defined as: (a) the correlation between regional mean time-series; and (b) the focal maximum of voxel-wise correlations within FreeSurfer regions. The association of age and gender with each functional connection was determined using linear regression. The preprocessing included the exclusion of children with excessive head motion and scrubbing to reduce the influence of minor head motion during scanning.

RESULTS

Cortico-cortical associations echoed previous findings that connectivity shifts from short to long-range with age. Subcortico-cortical associations with age were primarily negative in the focal network approach but were both positive and negative in the mean time-series network approach. Between subcortical regions, age-related associations were negative in both network approaches. Few connections had significant associations with gender.

CONCLUSIONS

The present study replicates previously reported age-related patterns of connectivity in a relatively narrow age-range of children. In addition, we extended these findings by demonstrating decreased connectivity within the subcortex with increasing age. Lastly, we show the utility of a more focal approach that challenges the spatial assumptions made by the traditional mean time series approach.

Static and dynamic connectomics differentiate between depressed patients with and without suicidal ideation

Neural circuit dysfunction underlies the biological mechanisms of suicidal ideation (SI). However, little is known about how the brain's "dynome" differentiate between depressed patients with and without SI. This study included depressed patients (n = 48) with SI, without SI (NSI), and healthy controls (HC, n = 30). All participants underwent resting-state functional magnetic resonance imaging. We constructed dynamic and static connectomics on 200 nodes using a sliding window and full-length time-series correlations, respectively. Specifically, the temporal variability of dynamic connectomic was quantified using the variance of topological properties across sliding window. The overall topological properties of both static and dynamic connectomics further differentiated between SI and NSI, and also predicted the severity of SI. The SI showed decreased overall topological properties of static connectomic relative to the HC. The SI exhibited increases in overall topological properties with regard to the dynamic connectomic when compared with the HC and the NSI. Importantly, combining the overall topological properties of dynamic and static connectomics yielded mean 75% accuracy (all p < .001) with mean 71% sensitivity and mean 75% specificity in differentiating between SI and NSI. Moreover, these features may predict the severity of SI (mean r = .55, all p < .05). The findings revealed that combining static and dynamic connectomics could differentiate between SI and NSI, offering new insight into the physiopathological mechanisms underlying SI. Furthermore, combining the brain's connectome and dynome may be considered a neuromarker for diagnostic and predictive models in the study of SI.

Different brain networks underlying intelligence in autism spectrum disorders

There has been sustained clinical and cognitive neuroscience research interest in how network correlates of brain-behavior relationships might be altered in Autism Spectrum Disorders (ASD) and other neurodevelopmental disorders. As previous work has mostly focused on adults, the nature of whole-brain connectivity networks underlying intelligence in pediatric cohorts with abnormal neurodevelopment requires further investigation. We used network-based statistics (NBS) to examine the association between resting-state functional Magnetic Resonance Imaging (fMRI) connectivity and fluid intelligence ability in male children (n = 50) with Autism Spectrum Disorders (ASD; M = 10.45, SD = 1.58 years and in controls (M = 10.38, SD = 0.96 years) matched on fluid intelligence performance, age and sex. Repeat analyses were performed in independent sites for validation and replication. Despite being equivalent on fluid intelligence ability to strictly matched neurotypical controls, boys with ASD displayed a subnetwork of significantly increased associations between functional connectivity and fluid intelligence. Between-group differences remained significant at higher edge thresholding, and results were validated in independent-site replication analyses in an equivalent age and sex-matched cohort with ASD. Regions consistently implicated in atypical connectivity correlates of fluid intelligence in ASD were the angular gyrus, posterior middle temporal gyrus, occipital and temporo-occipital regions. Development of fluid intelligence neural correlates in young ASD males is aberrant, with an increased strength in intrinsic connectivity association during childhood. Alterations in whole-brain network correlates of fluid intelligence in ASD may be a compensatory mechanism that allows equal task performance to neurotypical peers.

Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity

A common finding in human functional brain-imaging studies is that damage to neural systems paradoxically results in enhanced functional connectivity between network regions, a phenomenon commonly referred to as 'hyperconnectivity'. Here, we describe the various ways that hyperconnectivity operates to benefit a neural network following injury while simultaneously negotiating the trade-off between metabolic cost and communication efficiency. Hyperconnectivity may be optimally expressed by increasing connections through the most central and metabolically efficient regions (i.e., hubs). While adaptive in the short term, we propose that chronic hyperconnectivity may leave network hubs vulnerable to secondary pathological processes over the life span due to chronically elevated metabolic stress. We conclude by offering novel, testable hypotheses for advancing our understanding of the role of hyperconnectivity in systems-level brain plasticity in neurological disorders.

Advances in Neuroimaging for Neurodegenerative Disease

This chapter is intended as a primer to the most widely used neuroimaging methods available in the prediction, diagnosis and monitoring of the neurodegenerative diseases. We describe the imaging methods that allow us to examine brain structure, function and pathology and investigate neurodegenerative mechanisms in vivo. We describe methods to interrogate brain structure with magnetic resonance imaging (MRI), and brain function with molecular imaging, functional MRI and electro- and magneto-encephalography. We highlight the major neuroimaging advances, including brain stimulation and connectomics, which have brought new insights into a wide range of neurodegenerative diseases and describe some of the challenges in imaging clinical populations. Finally, we discuss the future of neuroimaging in neurodegenerative disease and its potential for generating predictive, diagnostic and prognostic biomarkers.