Resting-state functional connectivity reflects structural connectivity in the default mode network

Altered Structural and Functional MRI Connectivity in Type 2 Diabetes Mellitus Related Cognitive Impairment: A Review

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment in many domains. There are several pieces of evidence that changes in neuronal neuropathies and metabolism have been observed in T2DM. Structural and functional MRI shows that abnormal connections and synchronization occur in T2DM brain circuits and related networks. Neuroplasticity and energy metabolism appear to be principal effector systems, which may be related to amyloid beta (Aβ) deposition, although there is no unified explanation that includes the complex etiology of T2DM with cognitive impairment. Herein, we assume that cognitive impairment in diabetes may lead to abnormalities in neuroplasticity and energy metabolism in the brain, and those reflected to MRI structural connectivity and functional connectivity, respectively.

The role of PFC networks in cognitive control and executive function

Systems neuroscience approaches with a focus on large-scale brain organization and network analysis are advancing foundational knowledge of how cognitive control processes are implemented in the brain. Over the past decade, technological and computational innovations in the study of brain connectivity have led to advances in our understanding of how brain networks function, inspiring new conceptualizations of the role of prefrontal cortex (PFC) networks in the coordination of cognitive control. In this review, we describe six key PFC networks involved in cognitive control and elucidate key principles relevant for understanding how these networks implement cognitive control. Implementation of cognitive control in a constantly changing environment depends on the dynamic and flexible organization of PFC networks. In this context, we describe major empirical and theoretical models that have emerged in recent years and describe how their functional architecture and dynamic organization supports flexible cognitive control. We take an overarching view of advances made in the past few decades and consider fundamental issues regarding PFC network function, global brain dynamics, and cognition that still need to be resolved. We conclude by clarifying important future directions for research on cognitive control and their implications for advancing our understanding of PFC networks in brain disorders.

Amygdala connectivity and implications for social cognition and disorders

The amygdala is a hub of subcortical region that is crucial in a wide array of affective and motivation-related behaviors. While early research contributed significantly to our understanding of this region's extensive connections to other subcortical and cortical regions, recent methodological advances have enabled researchers to better understand the details of these circuits and their behavioral contributions. Much of this work has focused specifically on investigating the role of amygdala circuits in social cognition. In this chapter, we review both long-standing knowledge and novel research on the amygdala's structure, function, and involvement in social cognition. We focus specifically on the amygdala's circuits with the medial prefrontal cortex, the orbitofrontal cortex, and the hippocampus, as these regions share extensive anatomic and functional connections with the amygdala. Furthermore, we discuss how dysfunction in the amygdala may contribute to social deficits in clinical disorders including autism spectrum disorder, social anxiety disorder, and Williams syndrome. We conclude that social functions mediated by the amygdala are orchestrated through multiple intricate interactions between the amygdala and its interconnected brain regions, endorsing the importance of understanding the amygdala from network perspectives.

ENIGMA brain injury: Framework, challenges, and opportunities

Traumatic brain injury (TBI) is a major cause of disability worldwide, but the heterogeneous nature of TBI with respect to injury severity and health comorbidities make patient outcome difficult to predict. Injury severity accounts for only some of this variance, and a wide range of preinjury, injury-related, and postinjury factors may influence outcome, such as sex, socioeconomic status, injury mechanism, and social support. Neuroimaging research in this area has generally been limited by insufficient sample sizes. Additionally, development of reliable biomarkers of mild TBI or repeated subconcussive impacts has been slow, likely due, in part, to subtle effects of injury and the aforementioned variability. The ENIGMA Consortium has established a framework for global collaboration that has resulted in the largest-ever neuroimaging studies of multiple psychiatric and neurological disorders. Here we describe the organization, recent progress, and future goals of the Brain Injury working group.

Quantitative MRI Evidence for Cognitive Reserve in Healthy Elders and Prodromal Alzheimer's Disease

BACKGROUND

Cognitive reserve (CR) has been postulated to contribute to the variation observed between neuropathology and clinical outcomes in Alzheimer's disease (AD).

OBJECTIVE

We investigated the effect of an education-occupation derived CR proxy on biological properties of white matter tracts in patients with amnestic mild cognitive impairment (aMCI) and healthy elders (HC).

METHODS

Educational attainment and occupational complexity ratings (complexity with data, people, and things) from thirty-five patients with aMCI and twenty-eight HC were used to generate composite CR scores. Quantitative magnetic resonance imaging (qMRI) and multi-shell diffusion MRI were used to extract macromolecular tissue volume (MTV) across major white matter tracts.

RESULTS

We observed significant differences in the association between CR and white matter tract MTV in aMCI versus HC when age, gender, intracranial volume, and memory ability were held constant. Particularly, in aMCI, higher CR was associated with worse tract pathology (lower MTV) in the left and right dorsal cingulum, callosum forceps major, right inferior fronto-occipital fasciculus, and right superior longitudinal fasciculus (SLF) tracts. Conversely higher CR was associated with higher MTV in the right parahippocampal cingulum and left SLF in HC.

CONCLUSION

Our results support compensatory CR mechanisms in aMCI and neuroprotective mechanisms in HC and suggest differential roles for CR on white matter macromolecular properties in healthy elders versus prodromal AD patients.

TractEM: Evaluation of protocols for deterministic tractography white matter atlas

Reproducible identification of white matter pathways across subjects is essential for the study of structural connectivity of the human brain. One of the key challenges is anatomical differences between subjects and human rater subjectivity in labeling. Labeling white matter regions of interest presents many challenges due to the need to integrate both local and global information. Clearly communicating the manual processes to capture this information is cumbersome, yet essential to lay a solid foundation for comprehensive atlases. Segmentation protocols must be designed so the interpretation of the requested tasks as well as locating structural landmarks is anatomically accurate, intuitive and reproducible. In this work, we quantified the reproducibility of a first iteration of an open/public multi-bundle segmentation protocol. This allowed us to establish a baseline for its reproducibility as well as to identify the limitations for future iterations. The protocol was tested/evaluated on both typical 3 T research acquisition Baltimore Longitudinal Study of Aging (BLSA) and high-acquisition quality Human Connectome Project (HCP) datasets. The results show that a rudimentary protocol can produce acceptable intra-rater and inter-rater reproducibility. However, this work highlights the difficulty in generalizing reproducible results and the importance of reaching consensus on anatomical description of white matter pathways. The protocol has been made available in open source to improve generalizability and reliability in collaboration. The goal is to improve upon the first iteration and initiate a discussion on the anatomical validity (or lack thereof) of some bundle definitions and the importance of reproducibility of tractography segmentation.

Intrinsic Connectivity Networks of Glutamate-Mediated Antidepressant Response: A Neuroimaging Review

Conventional monoamine-based pharmacotherapy, considered the first-line treatment for major depressive disorder (MDD), has several challenges, including high rates of non-response. To address these challenges, preclinical and clinical studies have sought to characterize antidepressant response through monoamine-independent mechanisms. One striking example is glutamate, the brain's foremost excitatory neurotransmitter: since the 1990s, studies have consistently reported altered levels of glutamate in MDD, as well as antidepressant effects following molecular targeting of glutamatergic receptors. Therapeutically, this has led to advances in the discovery, testing, and clinical application of a wide array of glutamatergic agents, particularly ketamine. Notably, ketamine has been demonstrated to rapidly improve mood symptoms, unlike monoamine-based interventions, and the neurobiological basis behind this rapid antidepressant response is under active investigation. Advances in brain imaging techniques, including functional magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography, enable the identification of the brain network-based characteristics distinguishing rapid glutamatergic modulation from the effect of slow-acting conventional monoamine-based pharmacology. Here, we review brain imaging studies that examine brain connectivity features associated with rapid antidepressant response in MDD patients treated with glutamatergic pharmacotherapies in contrast with patients treated with slow-acting monoamine-based treatments. Trends in recent brain imaging literature suggest that the activity of brain regions is organized into coherent functionally distinct networks, termed intrinsic connectivity networks (ICNs). We provide an overview of major ICNs implicated in depression and explore how treatment response following glutamatergic modulation alters functional connectivity of limbic, cognitive, and executive nodes within ICNs, with well-characterized anti-anhedonic effects and the enhancement of "top-down" executive control. Alterations within and between the core ICNs could potentially exert downstream effects on the nodes within other brain networks of relevance to MDD that are structurally and functionally interconnected through glutamatergic synapses. Understanding similarities and differences in brain ICNs features underlying treatment response will positively impact the trajectory and outcomes for adults suffering from MDD and will facilitate the development of biomarkers to enable glutamate-based precision therapeutics.

Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study®

Background

Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health.

Methods

We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers.

Results

The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4–100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately.

Conclusions

The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society.

Hierarchical cortical networks of "voice patches" for processing voices in human brain

Humans have an extraordinary ability to recognize and differentiate voices. It is yet unclear whether voices are uniquely processed in the human brain. To explore the underlying neural mechanisms of voice processing, we recorded electrocorticographic signals from intracranial electrodes in epilepsy patients while they listened to six different categories of voice and nonvoice sounds. Subregions in the temporal lobe exhibited preferences for distinct voice stimuli, which were defined as "voice patches." Latency analyses suggested a dual hierarchical organization of the voice patches. We also found that voice patches were functionally connected under both task-engaged and resting states. Furthermore, the left motor areas were coactivated and correlated with the temporal voice patches during the sound-listening task. Taken together, this work reveals hierarchical cortical networks in the human brain for processing human voices.

Association Between Functional and Structural Brain Connectivity of the Default Mode Network in Non-treatment Seeking Individuals With Alcohol Use Disorder

AIMS

Alcohol use disorder (AUD) is associated with alterations within the default mode network (DMN) at rest. Also, impaired white matter structures have been observed in individuals with AUD. This study developed a workflow for examining the relation between functional and structural connectivity, exemplary for nodes of the DMN within a sample of non-treatment seeking individuals with AUD. Furthermore, AUD severity was correlated with both measures independently.

METHODS

The functional magnetic resonance imaging (fMRI) protocol included anatomical, resting state and diffusion weighted imaging measurements. Independent component analyses and deterministic fiber tracking as well as correlation analyses, including the severity of AUD, were performed. N = 18 out of 23 adult study participants took part in the fMRI examination, and N = 15 were included in the final analyses.

RESULTS

Established resting-state networks were reliably identified in our sample. Structural connections were found between several nodes of the DMN, whereas only fibers between the medial prefrontal cortex and the posterior cingulate cortex were reliably detected in all individuals. A negative correlation was observed between brain activation during rest and AUD severity in left parietal and temporal regions and the putamen. A more severe AUD predicted impairments in white matter integrity of the cingulum.

CONCLUSION

In AUD, information obtained from a combination of resting-state, diffusion weighted data and clinical information has great potential to provide a more profound understanding of the disorder since alterations may already become apparent at earlier stages of the disorder, e.g. in non-treatment seeking individuals.

SUMMARY

Alcohol use disorder leads to alterations in the default mode network of the resting brain that is associated with the severity of the disorder. Following our workflow, white matter impairments can be observed between some of the nodes of the default mode network using diffusion tensor imaging. Both, resting-state functional and structural connectivity relate to the severity of alcohol use disorder.

Increased ALFF and functional connectivity of the right striatum in bipolar disorder patients

BACKGROUND

Bipolar disorder (BD) is a serious neuropsychiatric disorder characterized by alternating periods of mania, depression, and euthymia. Abnormal spontaneous brain activity within the cortical-striatal neural circuits has been observed in patients with BD. However, whether the abnormality appears in patients with BD while not in a manic mood state is unclear.

METHODS

This study collected resting-state fMRI data from 65 patients with BD who were not in a manic mood state and 85 matched healthy controls. First, we examined differences in amplitude of low-frequency fluctuations (ALFF) between the patients with BD and the healthy controls to identify regions that show abnormal local spontaneous activity in the patients. Based on the ALFF results, we conducted seed-based resting-state functional connectivity (rsFC) analysis to identify the changes in brain networks that are centered on the regions showing abnormal local spontaneous activity in the patients. Finally, we repeated these analyses in a sub-sample comprising euthymic BD patients (N = 37) and between the euthymic BD patients and all the other patients who had at least mild depressive symptoms.

RESULTS

BD patients exhibited increased ALFF in the right caudate/putamen and increased rsFC in the right caudate/putamen with the right inferior parietal lobe (cluster-level FWE p < 0.05). Further analyses showed that the euthymic BD patients showed similar abnormalities in ALFF and rsFC maps as found in all patients with BD. And the euthymic BD patients were comparable with all the other patients who had at least mild depressive symptoms in ALFF values.

CONCLUSIONS

Our results indicated the important role of the right striatum in the baseline brain function of BD patients and suggested that the abnormality of spontaneous brain activity in the cortical-striatal neural circuits may be a trait-like variant in patients with BD. The results deepen our understanding of the neurobiological mechanisms associated with BD etiology.

Peering into the Brain through the Retrosplenial Cortex to Assess Cognitive Function of the Injured Brain

The retrosplenial cortex (RSC) is a posterior cortical area that has been drawing increasing interest in recent years, with a growing number of studies studying its contribution to cognitive and sensory functions. From an anatomical perspective, it has been established that the RSC is extensively and often reciprocally connected with the hippocampus, neocortex, and many midbrain regions. Functionally, the RSC is an important hub of the default-mode network. This endowment, with vast anatomical and functional connections, positions the RSC to play an important role in episodic memory, spatial and contextual learning, sensory-cognitive activities, and multi-modal sensory information processing and integration. Additionally, RSC dysfunction has been reported in cases of cognitive decline, particularly in Alzheimer's disease and stroke. We review the literature to examine whether the RSC can act as a cortical marker of persistent cognitive dysfunction after traumatic brain injury (TBI). Because the RSC is easily accessible at the brain's surface using in vivo techniques, we argue that studying RSC network activity post-TBI can shed light into the mechanisms of less-accessible brain regions, such as the hippocampus. There is a fundamental gap in the TBI field about the microscale alterations occurring post-trauma, and by studying the RSC's neuronal activity at the cellular level we will be able to design better therapeutic tools. Understanding how neuronal activity and interactions produce normal and abnormal activity in the injured brain is crucial to understanding cognitive dysfunction. By using this approach, we expect to gain valuable insights to better understand brain disorders like TBI.

Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements

To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.

Altered Amygdala-prefrontal Connectivity in Chronic Nonspecific Low Back Pain: Resting-state fMRI and Dynamic Causal Modelling Study

Chronic nonspecific low back pain (cNLBP) is a leading contributor to disease burden worldwide that is difficult to treat due to its nonspecific aetiology and complexity. The amygdala is a complex of structurally and functionally heterogeneous nuclei that serve as a key neural substrate for the interactions between pain and negative affective states. However, whether the functions of amygdalar subcomponents are differentially altered in cNLBP remains unknown. Little attention has focused on effective connectivity of the amygdala with the cortex in cNLBP. In this study, thirty-three patients with cNLBP and 33 healthy controls (HCs) were included. Resting-state functional connectivity (rsFC) and effective connectivity of the amygdala and its subregions were examined. Our results showed that the patient group exhibited significantly greater rsFC between the left amygdala and left dorsal medial prefrontal cortex (mPFC), which was negatively correlated with pain intensity ratings. Subregional analyses suggested a difference located at the superficial nuclei of the amygdala. Dynamic causal modelling revealed significantly lower effective connectivity from the left amygdala to the dorsal mPFC in patients with cNLBP than in HCs. Both groups exhibited stronger effective connectivity from the left amygdala to the right amygdala. In summary, these findings not only suggested altered rsFC of the amygdala-mPFC pathway in cNLBP but also implicated an abnormal direction of information processing between the amygdala and mPFC in these patients. Our results further highlight the involvement of the amygdala in the neuropathology of cNLBP.

Gyral Hinges Account for the Highest Cost and the Highest Communication Capacity in a Corticocortical Network

Prior studies reported the global structure of brain networks exhibits the "small-world" and "rich-world" attributes. However, the underlying structural and functional architecture highlighted by these graph theory findings hasn't been explicitly related to the morphology of the cortex. This could be attributed to the lower resolution of used folding patterns, such as gyro-sulcal patterns. By defining a novel gyral folding pattern, termed gyral hinge (GH), which is the conjunction of ordinary gyri from multiple directions, we found GHs possess the highest length and cost in the white matter fiber connective network, and the shortest paths in the network tend to travel through GHs in their middle part. Based on these findings, we would hypothesize GHs could reside in the centers of a network core, thereby accounting for the highest cost and the highest communication capacity in a corticocortical network. The following results further support our hypothesis: 1) GHs possess stronger functional network integration capacity. 2) Higher cost is found on the connection with GHs to hinges and GHs to GHs. 3) Moving GHs introduces higher extra network cost. Our findings and hypotheses could reveal a profound relationship among the cortical folding patterns, axonal wiring architectures, and brain functions.

Imaging modalities in differential diagnosis of Parkinson’s disease: opportunities and challenges

Background

Parkinson’s disease (PD) diagnosis is yet largely based on the related clinical aspects. However, genetics, biomarkers, and neuroimaging studies have demonstrated a confirming role in the diagnosis, and future developments might be used in a pre-symptomatic phase of the disease.

Main text

This review provides an update on the current applications of neuroimaging modalities for PD diagnosis. A literature search was performed to find published studies that were involved on the application of different imaging modalities for PD diagnosis. An organized search of PubMed/MEDLINE, Embase, ProQuest, Scopus, Cochrane, and Google Scholar was performed based on MeSH keywords and suitable synonyms. Two researchers (TM and JPI) independently and separately performed the literature search. Our search strategy in each database was done by the following terms: ((Parkinson [Title/Abstract]) AND ((“Parkinsonian syndromes ”[Mesh]) OR Parkinsonism [Title/Abstract])) AND ((PET [Title/Abstract]) OR “SPECT”[Mesh]) OR ((Functional imaging, Transcranial sonography [Title/Abstract]) OR “Magnetic resonance spectroscopy ”[Mesh]). Database search had no limitation in time, and our last update of search was in February 2021. To have a comprehensive search and to find possible relevant articles, a manual search was conducted on the reference list of the articles and limited to those published in English.

Conclusion

Early diagnosis of PD could be vital for early management and adequate neuroprotection. Recent neuroimaging modalities such as SPECT and PET imaging using radiolabeled tracers, MRI, and CT are used to discover the disease. By the modalities, it is possible to early diagnose dopaminergic degeneration and also to differentiate PD from others parkinsonian syndromes, to monitor the natural progression of the disease and the effect of neuroprotective treatments on the progression. In this regard, functional imaging techniques have provided critical insights and roles on PD.

Evidence for exercise-related plasticity in functional and structural neural network connectivity

The number of studies investigating exercise and cardiorespiratory fitness (CRF)-related changes in the functional and structural organization of brain networks continues to rise. Functional and structural connectivity are critical biomarkers for brain health and many exercise-related benefits on the brain are better represented by network dynamics. Here, we reviewed the neuroimaging literature to better understand how exercise or CRF may facilitate and maintain the efficiency and integrity of functional and structural aspects of brain networks in both younger and older adults. Converging evidence suggests that increased exercise performance and CRF modulate functional connectivity of the brain in a way that corresponds to behavioral changes such as cognitive and motor performance improvements. Similarly, greater physical activity levels and CRF are associated with better cognitive and motor function, which may be brought about by enhanced structural network integrity. This review will provide a comprehensive understanding of trends in exercise-network studies as well as future directions based on the gaps in knowledge that are currently present in the literature.

White matter integrity, suicidal ideation, and cognitive dysfunction in combat-exposed Iraq and Afghanistan Veterans

White matter alterations in frontolimbic circuits and poorer cognitive functioning have been observed in individuals endorsing suicidality across numerous psychiatric conditions. However, relationships between white matter integrity, cognition, and suicidality in Veterans are poorly understood, particularly for those at increased risk for suicide due to mental health conditions (e.g., posttraumatic stress disorder, depression) and traumatic brain injury history. We (1) examined white matter alterations in combat-exposed Iraq/Afghanistan Veterans with and without suicidal ideation (SI) and (2) investigated relationships between white matter integrity and neuropsychological functioning in regions that differed between SI groups. No group differences were found regarding psychiatric diagnoses. Participants with SI had lower fractional anisotropy (FA) in the posterior corona radiata, superior corona radiata, and superior longitudinal fasciculus relative to those without SI. Worse processing speed/attention performance was associated with lower FA in the superior longitudinal fasciculus, while worse executive functioning performance was associated with lower FA in the superior corona radiata and superior longitudinal fasciculus. Memory performance was not associated with FA. These findings suggest that white matter integrity may be involved in cognitive dysfunction and increased risk for SI. Interventions that target cognitive dysfunction may ameliorate SI, and in turn, reduce risk for suicide among Veterans.

The Role of the Human Brain Neuron-Glia-Synapse Composition in Forming Resting-State Functional Connectivity Networks

While significant progress has been achieved in studying resting-state functional networks in a healthy human brain and in a wide range of clinical conditions, many questions related to their relationship to the brain's cellular constituents remain. Here, we use quantitative Gradient-Recalled Echo (qGRE) MRI for mapping the human brain cellular composition and BOLD (blood-oxygen level-dependent) MRI to explore how the brain cellular constituents relate to resting-state functional networks. Results show that the BOLD signal-defined synchrony of connections between cellular circuits in network-defined individual functional units is mainly associated with the regional neuronal density, while the between-functional units' connectivity strength is also influenced by the glia and synaptic components of brain tissue cellular constituents. These mechanisms lead to a rather broad distribution of resting-state functional network properties. Visual networks with the highest neuronal density (but lowest density of glial cells and synapses) exhibit the strongest coherence of the BOLD signal as well as the strongest intra-network connectivity. The Default Mode Network (DMN) is positioned near the opposite part of the spectrum with relatively low coherence of the BOLD signal but with a remarkably balanced cellular contents, enabling DMN to have a prominent role in the overall organization of the brain and hierarchy of functional networks.

Parcellation-Free prediction of task fMRI activations from dMRI tractography

The relationship between brain structure and function plays a crucial role in cognitive and clinical neuroscience. We present a supervised machine learning based approach that captures this relationship by predicting the spatial extent of activations that are observed with task based functional Magnetic Resonance Imaging (fMRI) from the local white matter connectivity, as reflected in diffusion MRI (dMRI) tractography. In particular, we explore three different feature representations of local connectivity patterns that do not require a pre-defined parcellation of cortical and subcortical structures. Instead, they employ cluster-based Bag of Features, Gaussian Mixture Models, and Fisher vectors. We demonstrate that our framework can be used to test the statistical significance of structure-function relationships, compare it to parcellation-based and group-average benchmarks, and propose an algorithm for visualizing our chosen feature representations that permits a neuroanatomical interpretation of our results.

Electrophysiological Brain Changes Associated With Cognitive Improvement in a Pediatric Attention Deficit Hyperactivity Disorder Digital Artificial Intelligence-Driven Intervention: Randomized Controlled Trial

BACKGROUND

Cognitive stimulation therapy appears to show promising results in the rehabilitation of impaired cognitive processes in attention deficit hyperactivity disorder.

OBJECTIVE

Encouraged by this evidence and the ever-increasing use of technology and artificial intelligence for therapeutic purposes, we examined whether cognitive stimulation therapy implemented on a mobile device and controlled by an artificial intelligence engine can be effective in the neurocognitive rehabilitation of these patients.

METHODS

In this randomized study, 29 child participants (25 males) underwent training with a smart, digital, cognitive stimulation program (KAD_SCL_01) or with 3 commercial video games for 12 weeks, 3 days a week, 15 minutes a day. Participants completed a neuropsychological assessment and a preintervention and postintervention magnetoencephalography study in a resting state with their eyes closed. In addition, information on clinical symptoms was collected from the child´s legal guardians.

RESULTS

In line with our main hypothesis, we found evidence that smart, digital, cognitive treatment results in improvements in inhibitory control performance. Improvements were also found in visuospatial working memory performance and in the cognitive flexibility, working memory, and behavior and general executive functioning behavioral clinical indexes in this group of participants. Finally, the improvements found in inhibitory control were related to increases in alpha-band power in all participants in the posterior regions, including 2 default mode network regions of the interest: the bilateral precuneus and the bilateral posterior cingulate cortex. However, only the participants who underwent cognitive stimulation intervention (KAD_SCL_01) showed a significant increase in this relationship.

CONCLUSIONS

The results seem to indicate that smart, digital treatment can be effective in the inhibitory control and visuospatial working memory rehabilitation in patients with attention deficit hyperactivity disorder. Furthermore, the relation of the inhibitory control with alpha-band power changes could mean that these changes are a product of plasticity mechanisms or changes in the neuromodulatory dynamics.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN71041318; https://www.isrctn.com/ISRCTN71041318.

The role of multimodal MRI in mild cognitive impairment and Alzheimer's disease

BACKGROUND AND PURPOSE

Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD), where neurodegeneration is not as considerable, thereby potentially increasing the effect of treatments. Therefore, highly sensitive and specific classification of subjects with MCI is necessary, where various MRI modalities have displayed promise.

METHODS

Structural, diffusion, and resting-state (RS) functional MRI analyses were performed on the AD (n = 26), MCI (n = 5), and healthy control (HC) (n = 14) group. Structural analysis was performed via voxel-based morphometry (VBM) and volumetric subcortical segmentation analysis. Fractional anisotropy and mean diffusivity were estimated during the diffusion analysis. RS analysis investigated seed-based functional connectivity. Classification via support vector machine was performed to evaluate which MRI modality most accurately differentiated the groups. Multiple linear regression was conducted to evaluate the MRI modalities correlation with clinical assessment scores.

RESULTS

Classification of MCI and HC displayed highest accuracy based on diffusion MRI, which besides demonstrated high correlation with clinical scores. Classification was equally accurate in AD, when using VBM or diffusion tensor imaging measures. Yet, more variance was explained by VBM measures in the clinical assessment scores of the AD group.

CONCLUSIONS

This study highlights the potential of diffusion MRI in differentiating MCI from HC and AD. However, the results need to be interpreted with caution as sample size and artifacts in the MRI data probably influenced the results.

Brain controllability distinctiveness between depression and cognitive impairment

Alzheimer's disease (AD) is a progressive form of dementia marked by cognitive and memory deficits, estimated to affect ∼5.7 million Americans and account for ∼$277 billion in medical costs in 2018. Depression is one of the most common neuropsychiatric disorders that accompanies AD, appearing in up to 50% of patients. AD and Depression commonly occur together with overlapped symptoms (depressed mood, anxiety, apathy, and cognitive deficits.) and pose diagnostic challenges early in the clinical presentation. Understanding their relationship is critical for advancing treatment strategies, but the interaction remains poorly studied and thus often leads to a rapid decline in functioning. Modern systems and control theory offer a wealth of novel methods and concepts to assess the important property of a complex control system, such as the brain. In particular, the brain controllability analysis captures the ability to guide the brain behavior from an initial state (healthy or diseased) to a desired state in finite time, with suitable choice of inputs such as external or internal stimuli. The controllability property of the brain's dynamic processes will advance our understanding of the emergence and progression of brain diseases and thus helpful in the early diagnosis and novel treatment approaches. This study aims to assess the brain controllability differences between mild cognitive impairment (MCI), as prodromal AD, and Depression. This study used diffusion tensor imaging (DTI) data from 60 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI): 15 cognitively normal subjects and 45 patients with MCI, including 15 early MCI (EMCI) patients without depression, 15 EMCI patients with mild depression (EMCID), and 15 late MCI (LMCI) patients without depression. The structural brain network was firstly constructed and the brain controllability was characterized for each participant. The controllability of default mode network (DMN) and its sub-regions were then compared across groups in a structural basis. Results indicated that the brain average controllability of DMN in EMCI, LMCI, and EMCID were significantly decreased compared to healthy subjects (P < 0.05). The EMCI and LMCI groups also showed significantly greater average controllability of DMN versus the EMCID group. Furthermore, compared to healthy subjects, the regional controllability of the left/right superior prefrontal cortex and the left/right cingulate gyrus in the EMCID group showed a significant decrease (P < 0.01). Among these regions, the left superior prefrontal region's controllability was significantly decreased (P < 0.05) in the EMCID group compared with EMCI and LMCI groups. Our results provide a new perspective in understanding depressive symptoms in MCI patients and provide potential biomarkers for diagnosing depression from MCI and AD.

Capturing Neuroplastic Changes after iTBS in Patients with Post-Stroke Aphasia: A Pilot fMRI Study

Intermittent theta-burst stimulation (iTBS) is a high-efficiency transcranial magnetic stimulation (TMS) paradigm that has been applied to post-stroke aphasia (PSA). However, its efficacy mechanisms have not been clarified. This study aimed to explore the immediate effects of iTBS of the primary motor cortex (M1) of the affected hemisphere, on the functional activities and connectivity of the brains of PSA patients. A total of 16 patients with aphasia after stroke received iTBS with 800 pulses for 300 s. All patients underwent motor, language, and cognitive assessments and resting-state functional MRI scans immediately before and after the iTBS intervention. Regional, seed-based connectivity, and graph-based measures were used to test the immediate functional effects of the iTBS intervention, including the fractional amplitude of low-frequency fluctuation (fALFF), degree centrality (DC), and functional connectivity (FC) of the left M1 area throughout the whole brain. The results showed that after one session of iTBS intervention, the fALFF, DC, and FC values changed significantly in the patients' brains. Specifically, the DC values were significantly higher in the right middle frontal gyrus and parts of the left parietal lobe (p < 0.05), while fALFF values were significantly lower in the right medial frontal lobe and parts of the left intracalcarine cortex (p < 0.05), and the strength of the functional connectivity between the left M1 area and the left superior frontal gyrus was reduced (p < 0.05). Our findings provided preliminary evidences that the iTBS on the ipsilesional M1 could induce neural activity and functional connectivity changes in the motor, language, and other brain regions in patients with PSA, which may promote neuroplasticity and functional recovery.

Going round in circles-The Papez circuit in Alzheimer's disease

The hippocampus is regarded as the pivotal structure for episodic memory symptoms associated with Alzheimer's disease (AD) pathophysiology. However, what is often overlooked is that the hippocampus is 'only' one part of a network of memory critical regions, the Papez circuit. Other Papez circuit regions are often regarded as less relevant for AD as they are thought to sit 'downstream' of the hippocampus. However, this notion is oversimplistic, and increasing evidence suggests that other Papez regions might be affected before or concurrently with the hippocampus. In addition, AD research has mostly focused on episodic memory deficits, whereas spatial navigation processes are also subserved by the Papez circuit with increasing evidence supporting its valuable potential as a diagnostic measure of incipient AD pathophysiology. In the current review, we take a step forward analysing recent evidence on the structural and functional integrity of the Papez circuit across AD disease stages. Specifically, we will review the integrity of specific Papez regions from at-genetic-risk (APOE4 carriers), to mild cognitive impairment (MCI), to dementia stage of sporadic AD and autosomal dominant AD (ADAD). We related those changes to episodic memory and spatial navigation/orientation deficits in AD. Finally, we provide an overview of how the Papez circuit is affected in AD diseases and their specific symptomology contributions. This overview strengthened the need for moving away from a hippocampal-centric view to a network approach on how the whole Papez circuit is affected in AD and contributes to its symptomology, informing future research and clinical approaches.

Brain energy failure in dementia syndromes: Opportunities and challenges for glucagon-like peptide-1 receptor agonists

Medications for type 2 diabetes (T2DM) offer a promising path for discovery and development of effective interventions for dementia syndromes. A common feature of dementia syndromes is an energy failure due to reduced energy supply to neurons and is associated with synaptic loss and results in cognitive decline and behavioral changes. Among diabetes medications, glucagon‐like peptide‐1 (GLP‐1) receptor agonists (RAs) promote protective effects on vascular, microglial, and neuronal functions. In this review, we present evidence from animal models, imaging studies, and clinical trials that support developing GLP‐1 RAs for dementia syndromes. The review examines how changes in brain energy metabolism differ in conditions of insulin resistance and T2DM from dementia and underscores the challenges that arise from the heterogeneity of dementia syndromes. The development of GLP‐1 RAs as dementia therapies requires a deeper understanding of the regional changes in brain energy homeostasis guided by novel imaging biomarkers.

Neural contributors to trauma resilience: a review of longitudinal neuroimaging studies

Resilience in the face of major life stressors is changeable over time and with experience. Accordingly, differing sets of neurobiological factors may contribute to an adaptive stress response before, during, and after the stressor. Longitudinal studies are therefore particularly effective in answering questions about the determinants of resilience. Here we provide an overview of the rapidly-growing body of longitudinal neuroimaging research on stress resilience. Despite lingering gaps and limitations, these studies are beginning to reveal individual differences in neural circuit structure and function that appear protective against the emergence of future psychopathology following a major life stressor. Here we outline a neural circuit model of resilience to trauma. Specifically, pre-trauma biomarkers of resilience show that an ability to modulate activity within threat and salience networks predicts fewer stress-related symptoms. In contrast, early post-trauma biomarkers of subsequent resilience or recovery show a more complex pattern, spanning a number of major circuits including attention and cognitive control networks as well as primary sensory cortices. This novel synthesis suggests stress resilience may be scaffolded by stable individual differences in the processing of threat cues, and further buttressed by post-trauma adaptations to the stressor that encompass multiple mechanisms and circuits. More attention and resources supporting this work will inform the targets and timing of mechanistic resilience-boosting interventions.

How Inflammation Affects the Brain in Depression: A Review of Functional and Structural MRI Studies

This narrative review discusses how peripheral and central inflammation processes affect brain function and structure in depression, and reports on recent peripheral inflammatory marker-based functional and structural magnetic resonance imaging (MRI) studies from the perspective of neural-circuit dysfunction in depression. Chronic stress stimulates the activity of microglial cells, which increases the production of pro-inflammatory cytokines in the brain. In addition, microglial activation promotes a shift from the synthesis of serotonin to the synthesis of neurotoxic metabolites of the kynurenine pathway, which induces glutamate-mediated excitotoxicity in neurons. Furthermore, the region specificity of microglial activation is hypothesized to contribute to the vulnerability of specific brain regions in the depression-related neural circuits to inflammation-mediated brain injury. MRI studies are increasingly investigating how the blood levels of inflammatory markers such as C-reactive protein, interleukin (IL)-1β, IL-6, and tumor necrosis factor-α are associated with functional and structural neuroimaging markers in depression. Functional MRI studies have found that peripheral inflammatory markers are associated with aberrant activation patterns and altered functional connectivity in neural circuits involved in emotion regulation, reward processing, and cognitive control in depression. Structural MRI studies have suggested that peripheral inflammatory markers are related to reduced cortical gray matter and subcortical volumes, cortical thinning, and decreased integrity of white matter tracts within depression-related neural circuits. These neuroimaging findings may improve our understanding of the relationships between neuroinflammatory processes at the molecular level and macroscale in vivo neuralcircuit dysfunction in depression.

Global hippocampal and selective thalamic nuclei atrophy differentiate chronic TBI from Non-TBI

Traumatic brain injury (TBI) may increase susceptibility to neurodegenerative diseases later in life. One neurobiological parallel between chronic TBI and neurodegeneration may be accelerated aging and the nature of atrophy across subcortical gray matter structures. The main aim of the present investigation is to evaluate and rank the degree that subcortical gray matter atrophy differentiates chronic moderate-severe TBI from non-TBI participants by evaluating morphometric differences between groups. Forty individuals with moderate-severe chronic TBI (9.23 yrs from injury) and 33 healthy controls (HC) underwent high resolution 3D T1-weighted structural magnetic resonance imaging. Whole brain volume was classified into white matter, cortical and subcortical gray matter structures with hippocampi and thalami further segmented into subfields and nuclei, respectively. Extensive atrophy was observed across nearly all brain regions for chronic TBI participants. A series of multivariate logistic regression models identified subcortical gray matter structures of the hippocampus and thalamus as the most sensitive to differentiating chronic TBI from non-TBI participants (McFadden R² = .36, p < .001). Further analyses revealed the pattern of hippocampal atrophy to be global, occurring across nearly all subfields. The pattern of thalamic atrophy appeared to be much more selective and non-uniform, with largest between-group differences evident for nuclei bordering the ventricles. Subcortical gray matter was negatively correlated with time since injury (r = -.31, p = .054), while white matter and cortical gray matter were not. Cognitive ability was lower in the chronic TBI group (Cohen's d = .97, p = .003) and correlated with subcortical structures including the pallidum (r² = .23, p = .038), thalamus (r² = .36, p = .007) and ventral diencephalon (r² = .23, p = .036). These data may support an accelerated aging hypothesis in chronic moderate-severe TBI that coincides with a similar neuropathological profile found in neurodegenerative diseases.

Where We Mentalize: Main Cortical Areas Involved in Mentalization

When discussing “mentalization,” we refer to a very special ability that only humans and few species of great apes possess: the ability to think about themselves and to represent in their mind their own mental state, attitudes, and beliefs and those of others. In this review, a summary of the main cortical areas involved in mentalization is presented. A thorough literature search using PubMed MEDLINE database was performed. The search terms “cognition,” “metacognition,” “mentalization,” “direct electrical stimulation,” “theory of mind,” and their synonyms were combined with “prefrontal cortex,” “temporo-parietal junction,” “parietal cortex,” “inferior frontal gyrus,” “cingulate gyrus,” and the names of other cortical areas to extract relevant published papers. Non-English publications were excluded. Data were extracted and analyzed in a qualitative manner. It is the authors' belief that knowledge of the neural substrate of metacognition is essential not only for the “neuroscientist” but also for the “practical neuroscientist” (i.e., the neurosurgeon), in order to better understand the pathophysiology of mentalizing dysfunctions in brain pathologies, especially those in which integrity of cortical areas or white matter connectivity is compromised. Furthermore, in the context of neuro-oncological surgery, understanding the anatomical structures involved in the theory of mind can help the neurosurgeon obtain a wider and safer resection. Though beyond of the scope of this paper, an important but unresolved issue concerns the long-range white matter connections that unify these cortical areas and that may be themselves involved in neural information processing.

Comparison of the decreases in regional cerebral blood flow in the posterior cingulate cortex, precuneus, and parietal lobe between suspected non-Alzheimer's disease pathophysiology and Alzheimer's disease

BACKGROUND

Suspected non-Alzheimer's disease pathophysiology (SNAP) shows Alzheimer's disease (AD)-like neurodegeneration; however, amyloid β, which is a biological marker in AD, remains within normal levels. Since the effectiveness of anti-dementia drugs for AD on SNAP is unknown, it is important to distinguish between patients with SNAP and AD. We aimed to compare decreases in regional cerebral blood flow (rCBF) of the posterior cingulate cortex (PCC), precuneus, and parietal lobe critical to AD between SNAP and AD groups using the easy Z-score imaging system in single-photon emission computed tomography (eZIS-SPECT).

METHODS

We retrospectively analysed eZIS-SPECT data of 13 SNAP and 24 AD patients. The three indicators (severity, extent, and ratio) that distinguished AD patients from healthy controls in previous studies were automatically calculated and were compared between the SNAP and AD groups. Receiver operating characteristic curve analysis and the area under the curve (AUC) were used to evaluate the diagnostic performance of the three indicators of eZIS in discriminating between the two groups.

RESULTS

The mean values of severity, extent, and ratio were significantly lower in the SNAP group than in the AD group (P = 0.024, P = 0.044, and P = 0.045, respectively). The AUC values for severity, extent, and ratio were 0.668, 0.683, and 0.692, respectively.

CONCLUSIONS

The present study suggests that SNAP shows milder reduction of rCBF in the PCC, precuneus, and parietal lobe as compared to AD. However, it may be difficult to distinguish between SNAP and AD with the degrees of decrease in rCBF in these regions.

Neural signatures of hyperdirect pathway activity in Parkinson's disease

Parkinson's disease (PD) is characterised by the emergence of beta frequency oscillatory synchronisation across the cortico-basal-ganglia circuit. The relationship between the anatomy of this circuit and oscillatory synchronisation within it remains unclear. We address this by combining recordings from human subthalamic nucleus (STN) and internal globus pallidus (GPi) with magnetoencephalography, tractography and computational modelling. Coherence between supplementary motor area and STN within the high (21-30 Hz) but not low (13-21 Hz) beta frequency range correlated with 'hyperdirect pathway' fibre densities between these structures. Furthermore, supplementary motor area activity drove STN activity selectively at high beta frequencies suggesting that high beta frequencies propagate from the cortex to the basal ganglia via the hyperdirect pathway. Computational modelling revealed that exaggerated high beta hyperdirect pathway activity can provoke the generation of widespread pathological synchrony at lower beta frequencies. These findings suggest a spectral signature and a pathophysiological role for the hyperdirect pathway in PD.

Improvement of Tardive Dyskinesia during Mindfulness Meditation

Background: We report the case of a patient presenting with orofacial tardive dyskinesia (TD), following administration of a first-generation antipsychotic (Loxapine). Intervention: Four weeks of repeated sessions of mindfulness-based cognitive therapy (MBCT) and mindfulness-based stress reduction (MBSR) protocols were administered, with TD hetero-quantified before and during each session via the Abnormal Involuntary Movement Scale (AIMS). Results: The dyskinesia ameliorated quantitatively and qualitatively (1) during each session, and (2) at resting conditions in the long term. During some sessions, after which patients’ compliance was auto-evaluated as maximal, complete arrest of the TD was observed. Hypothesis and Conclusion: We suggest mindfulness meditation as a novel adjunctive therapeutic approach for tardive dyskinesia, and invite for further clinical and neurological investigations.

Changes in Default Mode Network Connectivity in Resting-State fMRI in People with Mild Dementia Receiving Cognitive Stimulation Therapy

Group cognitive stimulation therapy (CST) is a 7-week activity-based non-pharmacological intervention for people with mild to moderate dementia. Despite consistent evidence of clinical efficacy, the cognitive and brain mechanisms of CST remain unclear. Theoretically, group CST as a person-centred approach may work through promoting social interaction and personhood, executive function, and language use, especially in people with higher brain/cognitive reserve. To explore these putative mechanisms, structural MRI and resting-state functional MRI data were collected from 16 people with mild dementia before and after receiving CST, and in 13 dementia controls who received treatment as usual (TAU). Voxel-based morphometry (VBM) and resting-state functional connectivity (rs-FC) analyses were performed. Compared with TAU, the CST group maintained the total brain volume/total intracranial volume (TBV/TICV) ratio. Increased rs-FC in the default mode network (DMN) in the posterior cingulate cortex and bilateral parietal cortices nodes was observed in the CST over TAU groups between pre- and post-intervention timepoints. We provided preliminary evidence that CST maintains/enhances brain reserve both structurally and functionally. Considering the role of DMN in episodic memory retrieval and mental self-representation, preservation of personhood may be an important mechanism of CST for further investigation.

Structural and Functional Default Mode Network Connectivity and Antipsychotic Treatment Response in Medication-Naïve First Episode Psychosis Patients

Introduction

Only a few studies have comprehensively characterized default mode network (DMN) pathology on a structural and functional level, and definite conclusions cannot be drawn due to antipsychotic medication exposure and illness chronicity. The objective of this study was to characterize DMN pathology in medication-naïve first episode psychosis (FEP) patients, and determine if DMN structural and functional connectivity (FC) have potential utility as a predictor for subsequent antipsychotic treatment response.

Methods

Diffusion imaging and resting state FC data from 42 controls and 52 FEP were analyzed. Patients then received 16 weeks of antipsychotic treatment. Using region of interest analyses, we quantified FC of the DMN and structural integrity of the white matter tracts supporting DMN function. We then did linear regressions between DMN structural and FC indices and antipsychotic treatment response.

Results

We detected reduced DMN fractional anisotropy and axial diffusivity in FEP compared to controls. No DMN FC abnormalities nor correlations between DMN structural and FC were found. Finally, DMN fractional anisotropy and radial diffusivity were associated with response to treatment.

Conclusion

Our study highlights the critical role of the DMN in the pathophysiology suggesting that axonal damage may already be present in FEP patients. We also demonstrated that DMN pathology is clinically relevant, as greater structural DMN alterations were associated with a less favorable clinical response to antipsychotic medications.

Preventing Loss of Independence through Exercise (PLIÉ): A Pilot Trial in Older Adults with Subjective Memory Decline and Mild Cognitive Impairment

BACKGROUND

Preventing Loss of Independence through Exercise (PLIÉ) is a group movement program initially developed for people with mild-to-moderate dementia that integrates principles from several well-established traditions to specifically address the needs of people with cognitive impairment.

OBJECTIVE

To investigate whether PLIÉ would benefit cognitive and behavioral outcomes and functional brain connectivity in older adults with milder forms of cognitive impairment.

METHODS

Participants (≥55 y) with subjective memory decline (SMD) or mild cognitive impairment (MCI) were assessed with tests of cognitive and physical function, self-report questionnaires, and resting state functional magnetic resonance imaging (rs-fMRI) on a 3 Tesla scanner before and after participating in twice weekly PLIÉ classes for 12 weeks at the San Francisco Veterans Affairs Medical Center.

RESULTS

Eighteen participants completed the pre-post intervention pilot trial. We observed significant improvements on the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-cog; effect size 0.34, p = 0.002) and enhanced functional connections between the medial prefrontal cortex (mPFC) and other nodes of the default mode network (DMN) after PLIÉ. Improvements (i.e., lower scores) on ADAS-cog were significantly correlated with enhanced functional connectivity between the mPFC and left lateral parietal cortex (Spearman's ρ= -0.74, p = 0.001) and between the mPFC and right hippocampus (Spearman's ρ= -0.83, p = 0.001). After completing PLIÉ, participants reported significant reductions in feelings of social isolation and improvements in well-being and interoceptive self-regulation.

CONCLUSION

These preliminary findings of post-PLIÉ improvements in DMN functional connectivity, cognition, interoceptive self-regulation, well-being and reduced feelings of social isolation warrant larger randomized, controlled trials of PLIÉ in older adults with SMD and MCI.

Spectral dynamic causal modelling in healthy women reveals brain connectivity changes along the menstrual cycle

Longitudinal menstrual cycle studies allow to investigate the effects of ovarian hormones on brain organization. Here, we use spectral dynamic causal modelling (spDCM) in a triple network model to assess effective connectivity changes along the menstrual cycle within and between the default mode, salience and executive control networks (DMN, SN, and ECN). Sixty healthy young women were scanned three times along their menstrual cycle, during early follicular, pre-ovulatory and mid-luteal phase. Related to estradiol, right before ovulation the left insula recruits the ECN, while the right middle frontal gyrus decreases its connectivity to the precuneus and the DMN decouples into anterior/posterior parts. Related to progesterone during the mid-luteal phase, the insulae (SN) engage to each other, while decreasing their connectivity to parietal ECN, which in turn engages the posterior DMN. When including the most confident connections in a leave-one out cross-validation, we find an above-chance prediction of the left-out subjects' cycle phase. These findings corroborate the plasticity of the female brain in response to acute hormone fluctuations and may help to further understand the neuroendocrine interactions underlying cognitive changes along the menstrual cycle.

What have we really learned from functional connectivity in clinical populations?

Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.

Cortical connectivity in the face of congenital structural changes-A case of homozygous LAMC3 mutation

The homozygous LAMC3 gene mutation is associated with severe bilateral smoothening and thickening of the lateral occipital cortex . Despite this and further significant changes in gray matter structure, a patient harboring this mutation exhibited a range of remarkably intact perceptual abilities . One possible explanation of this perceptual sparing could be that the white matter structural integrity and functional connectivity in relevant pathways remained intact. To test this idea, we used diffusion tensor and functional magnetic resonance imaging to investigate functional connectivity in resting-state networks in major structural pathways involved in object perception and visual attention and corresponding microstructural integrity in a patient with homozygous LAMC3 mutation and sex, age, education, and socioeconomically matched healthy control group. White matter microstructural integrity results indicated widespread disruptions in both intra- and interhemispheric structural connections except inferior longitudinal fasciculus. With a few exceptions, the functional connectivity between the patient's adjacent gray matter regions of major white matter tracts of interest was conserved. In addition, functional localizers for face, object, and place areas showed similar results with a representative control, providing an explanation for the patient's intact face, place, and object recognition abilities. To generalize this finding, we also compared functional connectivity between early visual areas and face, place, and object category-selective areas, and we found that the functional connectivity of the patient was not different from the control group. Overall, our results provided complementary information about the effects of LAMC3 gene mutation on the human brain including intact temporo-occipital structural and functional connectivity that are compatible with preserved perceptual abilities.

Exploring brain connectivity changes in major depressive disorder using functional-structural data fusion: A CAN-BIND-1 study

There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT‐awFC). The novel FATCAT‐awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN‐BIND‐1) study. Large‐scale resting‐state networks were assessed. We found statistically significant anatomically‐weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region‐pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression.

Determination of Dynamic Brain Connectivity via Spectral Analysis

Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.

Altered cerebellar-cortical resting-state functional connectivity in cannabis users

BACKGROUND

Cannabis use has been associated with abnormalities in cerebellar mediated motor and non-motor (i.e. cognition and personality) phenomena. Since the cerebellum is a region with high cannabinoid type 1 receptor density, these impairments may reflect alterations of signaling between the cerebellum and other brain regions.

AIMS

We hypothesized that cerebellar-cortical resting-state functional connectivity (rsFC) would be altered in cannabis users, relative to their non-using peers. It was also hypothesized that differences in rsFC would be associated with cannabis use features, such as age of initiation and lifetime use.

METHODS

Cerebellar-cortical and subcortical rsFCs were computed between 28 cerebellar lobules, defined by a spatially unbiased atlas template of the cerebellum, and individual voxels in the cerebral regions, in 41 regular cannabis users (20 female) and healthy non-using peers (N = 31; 18 female). We also investigated associations between rsFC and cannabis use features (e.g. lifetime cannabis use and age of initiation).

RESULTS

Cannabis users demonstrated hyperconnectivity between the anterior cerebellar regions (i.e. lobule I-IV) with the posterior cingulate cortex, and hypoconnectivity between the rest of the cerebellum (i.e. Crus I and II, lobule VIIb, VIIIa, VIIIb, IX, and X) and the cortex. No associations were observed between features of cannabis use and rsFC.

CONCLUSIONS

Cannabis use was associated with altered patterns of rsFC from the cerebellum to the cerebral cortex which may have a downstream impact on behavior and cognition.

Classification of Adolescent Major Depressive Disorder Via Static and Dynamic Connectivity

This paper introduces an approach for classifying adolescents suffering from MDD using resting-state fMRI. Accurate diagnosis of MDD involves interviews with adolescent patients and their parents, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), behavioral observation as well as the experience of a clinician. Discovering predictive biomarkers for diagnosing MDD patients using functional magnetic resonance imaging (fMRI) scans can assist the clinicians in their diagnostic assessments. This paper investigates various static and dynamic connectivity measures extracted from resting-state fMRI for assisting with MDD diagnosis. First, absolute Pearson correlation matrices from 85 brain regions are computed and they are used to calculate static features for predicting MDD. A predictive sub-network extracted using sub-graph entropy classifies adolescent MDD vs. typical healthy controls with high accuracy, sensitivity and specificity. Next, approaches utilizing dynamic connectivity are employed to extract tensor based, independent component based and principal component based subject specific attributes. Finally, features from static and dynamic approaches are combined to create a feature vector for classification. A leave-one-out cross-validation method is used for the final predictor performance. Out of 49 adolescents with MDD and 33 matched healthy controls, a support vector machine (SVM) classifier using a radial basis function (RBF) kernel using differential sub-graph entropy combined with dynamic connectivity features classifies MDD vs. healthy controls with an accuracy of 0.82 for leave-one-out cross-validation. This classifier has specificity and sensitivity of 0.79 and 0.84, respectively.

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life.

The Motor Basis for Misophonia

Misophonia is a common disorder characterized by the experience of strong negative emotions of anger and anxiety in response to certain everyday sounds, such as those generated by other people eating, drinking, and breathing. The commonplace nature of these "trigger" sounds makes misophonia a devastating disorder for sufferers and their families. How such innocuous sounds trigger this response is unknown. Since most trigger sounds are generated by orofacial movements (e.g., chewing) in others, we hypothesized that the mirror neuron system related to orofacial movements could underlie misophonia. We analyzed resting state fMRI (rs-fMRI) connectivity (N = 33, 16 females) and sound-evoked fMRI responses (N = 42, 29 females) in misophonia sufferers and controls. We demonstrate that, compared with controls, the misophonia group show no difference in auditory cortex responses to trigger sounds, but do show: (1) stronger rs-fMRI connectivity between both auditory and visual cortex and the ventral premotor cortex responsible for orofacial movements; (2) stronger functional connectivity between the auditory cortex and orofacial motor area during sound perception in general; and (3) stronger activation of the orofacial motor area, specifically, in response to trigger sounds. Our results support a model of misophonia based on "hyper-mirroring" of the orofacial actions of others with sounds being the "medium" via which action of others is excessively mirrored. Misophonia is therefore not an abreaction to sounds, per se, but a manifestation of activity in parts of the motor system involved in producing those sounds. This new framework to understand misophonia can explain behavioral and emotional responses and has important consequences for devising effective therapies.SIGNIFICANCE STATEMENT Conventionally, misophonia, literally "hatred of sounds" has been considered as a disorder of sound emotion processing, in which "simple" eating and chewing sounds produced by others cause negative emotional responses. Our data provide an alternative but complementary perspective on misophonia that emphasizes the action of the trigger-person rather than the sounds which are a byproduct of that action. Sounds, in this new perspective, are only a "medium" via which action of the triggering-person is mirrored onto the listener. This change in perspective has important consequences for devising therapies and treatment methods for misophonia. It suggests that, instead of focusing on sounds, which many existing therapies do, effective therapies should target the brain representation of movement.

Goal setting and attaining: Neural correlates of positive coping style and hope

Trait hope focuses on individual goal-related thoughts and is assumed to be a critical indicator for one's mental health. However, the neurobiological basis of hope and the neurological mechanisms underlying the relationship between positive coping style (PCS) and hope (including the two dimensions of pathway thinking and agency thinking) are still largely unknown. Thus, this study explored the neural basis of trait hope by correlating the regional amplitude of low-frequency fluctuations (ALFF) and resting-state functional connectivity (RSFC) with the self-reported hope of 576 healthy first-year college students underwent RS-fMRI. Our results showed that trait hope was positively associated with PCS. A whole-brain correlation analysis provided early evidence that higher levels of trait hope were associated with decreased ALFF in the left frontal pole cortex (FPC). Additionally, pathway thinking was associated with decreased ALFF in FPC, increased ALFF in the right postcentral gyrus (PCG), decreased RSFC of the left FPC and left posterior cingulate cortex, the left FPC and right middle temporal gyrus, and the right PCG and left cerebellum. Furthermore, mediation analyses demonstrated that the PCG-cerebellum connectivity might link to pathway thinking through PCS and PCS might relate to trait hope through PCG-cerebellum connectivity. Our findings contribute to the neurobiological basis of hope and the neural mechanism underlying the relationship between trait hope and coping style.

Thalamus and claustrum control parallel layer 1 circuits in retrosplenial cortex

The granular retrosplenial cortex (RSG) is critical for both spatial and non-spatial behaviors, but the underlying neural codes remain poorly understood. Here, we use optogenetic circuit mapping in mice to reveal a double dissociation that allows parallel circuits in superficial RSG to process disparate inputs. The anterior thalamus and dorsal subiculum, sources of spatial information, strongly and selectively recruit small low-rheobase (LR) pyramidal cells in RSG. In contrast, neighboring regular-spiking (RS) cells are preferentially controlled by claustral and anterior cingulate inputs, sources of mostly non-spatial information. Precise sublaminar axonal and dendritic arborization within RSG layer 1, in particular, permits this parallel processing. Observed thalamocortical synaptic dynamics enable computational models of LR neurons to compute the speed of head rotation, despite receiving head direction inputs that do not explicitly encode speed. Thus, parallel input streams identify a distinct principal neuronal subtype ideally positioned to support spatial orientation computations in the RSG.