Quantifying the link between anatomical connectivity, gray matter volume and regional cerebral blood flow: an integrative MRI study

Towards a biologically annotated brain connectome

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

Quantitative association of cerebral blood flow, relaxation times and proton density in young and middle-aged primary insomnia patients: A prospective study using three-dimensional arterial spin labeling and synthetic magnetic resonance imaging

Objectives

To quantitatively measure the T1 value, T2 value, proton density (PD) value, and cerebral blood flow (CBF) in young and middle-aged primary insomnia (PI) patients, and analyze the correlations between relaxation times, PD, and CBF to explore potential brain changes.

Methods

Cranial magnetic resonance (MR) images of 44 PI patients and 30 healthy subjects were prospectively collected for analysis. The T1, T2, PD, and CBF values of the frontal lobe, parietal lobe, temporal lobe, and occipital lobe were independently measured using three-dimensional arterial spin labeling (3D-ASL), synthetic magnetic resonance imaging (syMRI) and a whole-brain automatic segmentation method. The differences of these imaging indices were compared between PI patients and healthy subjects. Follow-up MR images were obtained from PI patients after 6 months to compare with pre-treatment images. The Wilcoxon signed rank test and Spearman rank were used for statistical analysis.

Results

Bilateral CBF asymmetry was observed in 38 patients, with significant differences in both the T2 value and CBF between the four lobes of the brain (p < 0.01). However, no significant difference was found in the T1 and PD values between the bilateral lobes. A negative correlation was found between CBF and T2 values in the right four lobes of patients with primary insomnia (PI). During follow-up examinations, five PI patients showed a disappearance of insomnia symptoms and a decrease in CBF in both brain lobes.

Conclusion

Insomnia symptoms may be associated with high CBF, and most PI patients have higher CBF and lower T2 values in the right cerebral hemispheres. The right hemisphere appears to play a critical role in the pathophysiology of PI. The 3D-ASL and syMRI technologies can provide a quantitative imaging basis for investigating the brain conditions and changes in young and middle-aged PI patients.

Cerebral neurovascular alterations in stable chronic obstructive pulmonary disease: a preliminary fMRI study

Purpose

Cognitive impairment (CI) is very common in patients with chronic obstructive pulmonary disease (COPD). Cerebral structural and functional abnormalities have been reported in cognitively impaired patients with COPD, and the neurovascular coupling changes are rarely investigated. To address this issue, arterial spin labeling (ASL) and resting-state blood oxygenation level dependent (BOLD) fMRI techniques were used to determine whether any neurovascular changes in COPD patients.

Methods

Forty-five stable COPD patients and forty gender- and age-matched healthy controls were recruited. Furthermore, resting-state BOLD fMRI and ASL were acquired to calculate degree centrality (DC) and cerebral blood flow (CBF) respectively. The CBF-DC coupling and CBF/DC ratio were compared between the two groups.

Results

COPD patients showed abnormal CBF, DC and CBF/DC ratio in several regions. Moreover, lower CBF/DC ratio in the left lingual gyrus negatively correlated with naming scores, lower CBF/DC ratio in medial frontal cortex/temporal gyrus positively correlated with the Montreal Cognitive Assessment (MoCA), visuospatial/executive and delayed recall scores.

Conclusion

These findings may provide new potential insights into neuropathogenesis of cognition decline in stable COPD patients.

Multiple Cognitive and Behavioral Factors Link Association Between Brain Structure and Functional Impairment of Daily Instrumental Activities in Older Adults

OBJECTIVE

Functional impairment in daily activity is a cornerstone in distinguishing the clinical progression of dementia. Multiple indicators based on neuroimaging and neuropsychological instruments are used to assess the levels of impairment and disease severity; however, it remains unclear how multivariate patterns of predictors uniquely predict the functional ability and how the relative importance of various predictors differs.

METHOD

In this study, 881 older adults with subjective cognitive complaints, mild cognitive impairment (MCI), and dementia with Alzheimer's type completed brain structural magnetic resonance imaging (MRI), neuropsychological assessment, and a survey of instrumental activities of daily living (IADL). We utilized the partial least square (PLS) method to identify latent components that are predictive of IADL.

RESULTS

The result showed distinct brain components (gray matter density of cerebellar, medial temporal, subcortical, limbic, and default network regions) and cognitive-behavioral components (general cognitive abilities, processing speed, and executive function, episodic memory, and neuropsychiatric symptoms) were predictive of IADL. Subsequent path analysis showed that the effect of brain structural components on IADL was largely mediated by cognitive and behavioral components. When comparing hierarchical regression models, the brain structural measures minimally added the explanatory power of cognitive and behavioral measures on IADL.

CONCLUSION

Our finding suggests that cerebellar structure and orbitofrontal cortex, alongside with medial temporal lobe, play an important role in the maintenance of functional status in older adults with or without dementia. Moreover, the significance of brain structural volume affects real-life functional activities via disruptions in multiple cognitive and behavioral functions.

Efficient coding in the economics of human brain connectomics

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion-weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior-beyond the conventional network efficiency metric-for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

Rich-Club Connectivity of the Structural Covariance Network Relates to Memory Processes in Mild Cognitive Impairment and Alzheimer's Disease

BACKGROUND

Though mediotemporal lobe volume changes are well-known features of Alzheimer's disease (AD), grey matter volume changes may be distributed throughout the brain. These distributed changes are not independent due to the underlying network structure and can be described in terms of a structural covariance network (SCN).

OBJECTIVE

To investigate how the cortical brain organization is altered in AD we studied the mutual connectivity of hubs in the SCN, i.e., the rich-club.

METHODS

To construct the SCNs, cortical thickness was obtained from structural MRI for 97 participants (normal cognition, n = 37; mild cognitive impairment, n = 41; Alzheimer-type dementia, n = 19). Subsequently, rich-club coefficients were calculated from the SCN, and related to memory performance and hippocampal volume using linear regression.

RESULTS

Lower rich-club connectivity was related to lower memory performance as well as lower hippocampal volume.

CONCLUSION

Therefore, this study provides novel evidence of reduced connectivity in hub areas in relation to AD-related cognitive impairments and atrophy.

Where the genome meets the connectome: Understanding how genes shape human brain connectivity

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

Abnormal Cerebral Blood Flow and Functional Connectivity Strength in Subjects With White Matter Hyperintensities

White matter hyperintensities (WMHs) are common neuroimaging findings in the aging population and are associated with various clinical symptoms, especially cognitive impairment. Abnormal global cerebral blood flow (CBF) and specific functional connections have been reported in subjects with higher WMH loads. Nevertheless, the comprehensive functional mechanisms underlying WMH are yet to be established. In this study, by combining resting-state functional magnetic resonance imaging and arterial spin labeling, we investigated the neurovascular dysfunction in subjects with WMH in CBF, functional connectivity strength (FCS), and CBF–FCS coupling. The whole-brain alterations of all these measures were explored among non-dementia subjects with different WMH loads using a fine-grained Human Brainnetome Atlas. In addition, exploratory mediation analyses were conducted to further determine the relationships between these neuroimaging indicators, WMH load, and cognition. The results showed that subjects with higher WMH loads displayed decreased CBF and FCS mainly in regions involving the cognitive- and emotional-related brain networks, including the default mode network, salience network, and central executive network. Notably, subjects with higher WMH loads also showed an abnormal regional CBF–FCS coupling in several regions of the thalamus, posterior cingulate cortex, and parahippocampal gyrus involving the default mode network. Furthermore, regional CBF in the right inferior temporal gyrus and right dorsal caudate may mediate the relationship between WMH load and cognition in WMH subjects. These findings indicated characteristic changes in cerebral blood supply, brain activity, and neurovascular coupling in regions involving specific brain networks with the development of WMH, providing further information on pathophysiology underpinnings of the WMH and related cognitive impairment.

Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders

This proposed novel method consists of three levels of analyses of diffusion tensor imaging data: 1) voxel level analysis of fractional anisotropy of white matter tracks, 2) connection level analysis, based on fiber tracks between specific brain regions, and 3) network level analysis, based connections among multiple brain regions. Machine-learning techniques of (Fisher score) feature selection, (Support Vector Machine) pattern classification, and (Leave-one-out) cross-validation are performed, for recognition of the neural connectivity patterns for diagnostic purposes. For validation proposes, this multilevel approach achieved an average classification accuracy of 90% between Alzheimer's disease and healthy controls, 83% between Alzheimer's disease and mild cognitive impairment, and 83% between mild cognitive impairment and healthy controls. The results indicate that the multilevel diffusion tensor imaging approach used in this analysis is a potential diagnostic tool for clinical evaluations of brain disorders. The presented pipeline is now available as a tool for scientifically applications in a broad range of studies from both clinical and behavioral spectrum, which includes studies about autism, dyslexia, schizophrenia, dementia, motor body performance, among others.

Characterizing changes in network connectivity following chronic head trauma in special forces military personnel: a combined resting-fMRI and DTI study

BACKGROUND

Soldiers are exposed to significant repetitive head trauma, which may disrupt functional and structural brain connectivity patterns.

PURPOSE/HYPOTHESIS

Integrate resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) to characterize changes in connectivity biomarkers within Canadian Special Operations Forces (CANSOF), hypothesizing that alterations in architectural organization of cortical hubs may follow chronic repetitive head trauma.

METHODS

Fifteen CANSOFs with a history of chronic exposure to sub-concussive head trauma and concussive injuries (1.9 ± 2.0 concussions (range: [0-6])), as well as an equal age-matched cohort of controls (CTLs) were recruited. BOLD-based rs-fMRI was combined with DTI to reconstruct functional and structural networks using independent component analyses and probabilistic tractography. Connectivity markers were computed based on the distance between functional seeds to assess for possible differences in injury susceptibility of short- and long-range connections.

RESULTS/DISCUSSION

Significant hyper- and hypo-connectivity differences in cortical connections were observed suggesting that chronic head trauma may predispose soldiers to changes in the functional organization of brain networks. Significant structural alterations in axonal fibers directly connecting disrupted functional nodes were specific to hyper-connected long-range connections, suggesting a potential relationship between axonal injury and increases in neural recruitment following repetitive head trauma from high-exposure military duties.

Fractional Anisotropy changes in Parahippocampal Cingulum due to Alzheimer's Disease

Current treatments for Alzheimer's disease are only symptomatic and limited to reduce the progression rate of the mental deterioration. Mild Cognitive Impairment, a transitional stage in which the patient is not cognitively normal but do not meet the criteria for specific dementia, is associated with high risk for development of Alzheimer's disease. Thus, non-invasive techniques to predict the individual's risk to develop Alzheimer's disease can be very helpful, considering the possibility of early treatment. Diffusion Tensor Imaging, as an indicator of cerebral white matter integrity, may detect and track earlier evidence of white matter abnormalities in patients developing Alzheimer's disease. Here we performed a voxel-based analysis of fractional anisotropy in three classes of subjects: Alzheimer's disease patients, Mild Cognitive Impairment patients, and healthy controls. We performed Support Vector Machine classification between the three groups, using Fisher Score feature selection and Leave-one-out cross-validation. Bilateral intersection of hippocampal cingulum and parahippocampal gyrus (referred as parahippocampal cingulum) is the region that best discriminates Alzheimer's disease fractional anisotropy values, resulting in an accuracy of 93% for discriminating between Alzheimer's disease and controls, and 90% between Alzheimer's disease and Mild Cognitive Impairment. These results suggest that pattern classification of Diffusion Tensor Imaging can help diagnosis of Alzheimer's disease, specially when focusing on the parahippocampal cingulum.

Relationship between topological efficiency in white matter structural networks with cerebral oxygen metabolism in young adults

The relationship between the topological characteristics of the white matter (WM) network have been shown to be related to neural development, intelligence, and various diseases; however, few studies have been conducted to explore the relationship between topological characteristics of the WM network and cerebral metabolism. In a recent study we investigated the relationship between WM network topological and metabolic metrics of the cerebral parenchyma in healthy volunteers using the newly developed T2-relaxation-under-spin-tagging (TRUST) magnetic resonance imaging technique and graph theory approaches. Ninety-six healthy adults (25.5 ± 1.8 years of age) were recruited as volunteers in the current study. The cerebral metabolic rate of oxygen (CMRO₂), oxygen extraction fraction, and the global topological metrics of the WM network (global efficiency [E_glob], local efficiency, and small-worldliness) were assessed. A stepwise multiple linear regression model was estimated. CMRO₂ was entered as the dependent variable. The topological and demographic parameters (age, gender, FIQ, SBP, gray matter volume, and WM volume) were entered as independent variables in the model. The final performing models were comprised of predictors of E_glob, FIQ, and age (adjusted R² values were 0.489 [L-AAL] and 0.424 [H-1024]). Our study initially revealed a relationship between E_glob and cerebral oxygen metabolism in healthy young adults.

Strong-Weak Pruning for Brain Network Identification in Connectome-Wide Neuroimaging: Application to Amyotrophic Lateral Sclerosis Disease Stage Characterization

Magnetic resonance imaging allows acquiring functional and structural connectivity data from which high-density whole-brain networks can be derived to carry out connectome-wide analyses in normal and clinical populations. Graph theory has been widely applied to investigate the modular structure of brain connections by using centrality measures to identify the "hub" of human connectomes, and community detection methods to delineate subnetworks associated with diverse cognitive and sensorimotor functions. These analyses typically rely on a preprocessing step (pruning) to reduce computational complexity and remove the weakest edges that are most likely affected by experimental noise. However, weak links may contain relevant information about brain connectivity, therefore, the identification of the optimal trade-off between retained and discarded edges is a subject of active research. We introduce a pruning algorithm to identify edges that carry the highest information content. The algorithm selects both strong edges (i.e. edges belonging to shortest paths) and weak edges that are topologically relevant in weakly connected subnetworks. The newly developed "strong-weak" pruning (SWP) algorithm was validated on simulated networks that mimic the structure of human brain networks. It was then applied for the analysis of a real dataset of subjects affected by amyotrophic lateral sclerosis (ALS), both at the early (ALS2) and late (ALS3) stage of the disease, and of healthy control subjects. SWP preprocessing allowed identifying statistically significant differences in the path length of networks between patients and healthy subjects. ALS patients showed a decrease of connectivity between frontal cortex to temporal cortex and parietal cortex and between temporal and occipital cortex. Moreover, degree of centrality measures revealed significantly different hub and centrality scores between patient subgroups. These findings suggest a widespread alteration of network topology in ALS associated with disease progression.

On the Origin of Individual Functional Connectivity Variability: The Role of White Matter Architecture

Fingerprint patterns derived from functional connectivity (FC) can be used to identify subjects across groups and sessions, indicating that the topology of the brain substantially differs between individuals. However, the source of FC variability inferred from resting-state functional magnetic resonance imaging remains unclear. One possibility is that these variations are related to individual differences in white matter structural connectivity (SC). However, directly comparing FC with SC is challenging given the many potential biases associated with quantifying their respective strengths. In an attempt to circumvent this, we employed a recently proposed test-retest approach that better quantifies inter-subject variability by first correcting for intra-subject nuisance variability (i.e., head motion, physiological differences in brain state, etc.) that can artificially influence FC and SC measures. Therefore, rather than directly comparing the strength of FC with SC, we asked whether brain regions with, for example, low inter-subject FC variability also exhibited low SC variability. From this, we report two main findings: First, at the whole-brain level, SC variability was significantly lower than FC variability, indicating that an individual's structural connectome is far more similar to another relative to their functional counterpart even after correcting for noise. Second, although FC and SC variability were mutually low in some brain areas (e.g., primary somatosensory cortex) and high in others (e.g., memory and language areas), the two were not significantly correlated across all cortical and sub-cortical regions. Taken together, these results indicate that even after correcting for factors that may differently affect FC and SC, the two, nonetheless, remain largely independent of one another. Further work is needed to understand the role that direct anatomical pathways play in supporting vascular-based measures of FC and to what extent these measures are dictated by anatomical connectivity.

Altered Coupling Between Resting-State Cerebral Blood Flow and Functional Connectivity in Schizophrenia

BACKGROUND

Respective changes in resting-state cerebral blood flow (CBF) and functional connectivity in schizophrenia have been reported. However, their coupling alterations in schizophrenia remain largely unknown.

METHODS

89 schizophrenia patients and 90 sex- and age-matched healthy controls underwent resting-state functional MRI to calculate functional connectivity strength (FCS) and arterial spin labeling imaging to compute CBF. The CBF-FCS coupling of the whole gray matter and the CBF/FCS ratio (the amount of blood supply per unit of connectivity strength) of each voxel were compared between the 2 groups.

RESULTS

Whole gray matter CBF-FCS coupling was decreased in schizophrenia patients relative to healthy controls. In schizophrenia patients, the decreased CBF/FCS ratio was predominantly located in cognitive- and emotional-related brain regions, including the dorsolateral prefrontal cortex, insula, hippocampus and thalamus, whereas an increased CBF/FCS ratio was mainly identified in the sensorimotor regions, including the putamen, and sensorimotor, mid-cingulate and visual cortices.

CONCLUSION

These findings suggest that the neurovascular decoupling in the brain may be a possible neuropathological mechanism of schizophrenia.

In child and adult migraineurs the somatosensory cortex stands out … again: An arterial spin labeling investigation

Over the past decade, human brain imaging investigations have reported altered regional cerebral blood flow (rCBF) in the interictal phase of migraine. However, there have been conflicting findings across different investigations, making the use of perfusion imaging in migraine pathophysiology more difficult to define. These inconsistencies may reflect technical constraints with traditional perfusion imaging methods such as single-photon emission computed tomography and positron emission tomography. Comparatively, pseudocontinuous arterial spin labeling (pCASL) is a recently developed magnetic resonance imaging technique that is noninvasive and offers superior spatial resolution and increased sensitivity. Using pCASL, we have previously shown increased rCBF within the primary somatosensory cortex (S1) in adult migraineurs, where blood flow was positively associated with migraine frequency. Whether these observations are present in pediatric and young adult populations remains unknown. This is an important question given the age-related variants of migraine prevalence, symptomology, and treatments. In this investigation, we used pCASL to quantitatively compare and contrast blood flow within S1 in pediatric and young adult migraineurs as compared with healthy controls. In migraine patients, we found significant resting rCBF increases within bilateral S1 as compared with healthy controls. Furthermore, within the right S1, we report a positive correlation between blood flow value with migraine attack frequency and cutaneous allodynia symptom profile. Our results reveal that pediatric and young adult migraineurs exhibit analogous rCBF changes with adult migraineurs, further supporting the possibility that these alterations within S1 are a consequence of repeated migraine attacks. Hum Brain Mapp 38:4078-4087, 2017. © 2017 Wiley Periodicals, Inc.

Small-world human brain networks: Perspectives and challenges

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

Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images

The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a "proof of concept" about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis.

Asymmetric and Upper Body Parkinsonism in Patients with Idiopathic Normal-Pressure Hydrocephalus

Background and Purpose

Our aims were to analyze the characteristics of parkinsonian features and to characterize changes in parkinsonian motor symptoms before and after the cerebrospinal fluid tap test (CSFTT) in idiopathic normal-pressure hydrocephalus (INPH) patients.

Methods

INPH subjects were selected in consecutive order from a prospectively enrolled INPH registry. Fifty-five INPH patients (37 males) having a positive response to the CSFTT constituted the final sample for analysis. The mean age was 73.7±4.7 years. The pre-tap mean Unified Parkinson's Disease Rating Scale motor (UPDRS-III) score was 24.5±10.2.

Results

There was no significant difference between the upper and lower body UPDRS-III scores (p=0.174). The parkinsonian signs were asymmetrical in 32 of 55 patients (58.2%). At baseline, the Timed Up and Go Test and 10-meter walking test scores were positively correlated with the total motor score, global bradykinesia score, global rigidity score, upper body score, lower body score, and postural instability/gait difficulties score of UPDRS-III. After the CSFTT, the total motor score, global bradykinesia score, upper body score, and lower body score of UPDRS-III significantly improved (p<0.01). There was a significant decrease in the number of patients with asymmetric parkinsonism (p<0.05).

Conclusions

In the differential diagnosis of elderly patients presenting with asymmetric and upper body parkinsonism, we need to consider a diagnosis of INPH. The association between gait function and parkinsonism severity suggests the involvement of similar circuits producing gait and parkinsonian symptoms in INPH.

Basal Hippocampal Activity and Its Functional Connectivity Predicts Cocaine Relapse

BACKGROUND

Cocaine-induced neuroplastic changes may result in a heightened propensity for relapse. Using regional cerebral blood flow (rCBF) as a marker of basal neuronal activity, this study assessed alterations in rCBF and related resting state functional connectivity (rsFC) to prospectively predict relapse in patients following treatment for cocaine use disorder (CUD).

METHODS

Pseudocontinuous arterial spin labeling functional magnetic resonance imaging and resting blood oxygen level-dependent functional magnetic resonance imaging data were acquired in the same scan session in abstinent participants with CUD before residential treatment discharge and in 20 healthy matched control subjects. Substance use was assessed twice weekly following discharge. Relapsed participants were defined as those who used stimulants within 30 days following treatment discharge (n = 22); early remission participants (n = 18) did not.

RESULTS

Voxel-wise, whole-brain analysis revealed enhanced rCBF only in the left posterior hippocampus (pHp) in the relapsed group compared with the early remission and control groups. Using this pHp as a seed, increased rsFC strength with the posterior cingulate cortex (PCC)/precuneus was seen in the relapsed versus early remission subgroups. Together, both increased pHp rCBF and strengthened pHp-PCC rsFC predicted relapse with 75% accuracy at 30, 60, and 90 days following treatment.

CONCLUSIONS

In CUD participants at risk of early relapse, increased pHp basal activity and pHp-PCC circuit strength may reflect the propensity for heightened reactivity to cocaine cues and persistent cocaine-related ruminations. Mechanisms to mute hyperactivated brain regions and delink dysregulated neural circuits may prove useful to prevent relapse in patients with CUD.

3D interactive tractography-informed resting-state fMRI connectivity

In the past decade, the fusion between diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) has opened the way for exploring structure-function relationships in vivo. As it stands, the common approach usually consists of analysing fMRI and dMRI datasets separately or using one to inform the other, such as using fMRI activation sites to reconstruct dMRI streamlines that interconnect them. Moreover, given the large inter-individual variability of the healthy human brain, it is possible that valuable information is lost when a fixed set of dMRI/fMRI analysis parameters such as threshold values are assumed constant across subjects. By allowing one to modify such parameters while viewing the results in real-time, one can begin to fully explore the sensitivity of structure-function relations and how they differ across brain areas and individuals. This is especially important when interpreting how structure-function relationships are altered in patients with neurological disorders, such as the presence of a tumor. In this study, we present and validate a novel approach to achieve this: First, we present an interactive method to generate and visualize tractography-driven resting-state functional connectivity, which reduces the bias introduced by seed size, shape and position. Next, we demonstrate that structural and functional reconstruction parameters explain a significant portion of intra- and inter-subject variability. Finally, we demonstrate how our proposed approach can be used in a neurosurgical planning context. We believe this approach will promote the exploration of structure-function relationships in a subject-specific aspect and will open new opportunities for connectomics.

Cannabis, Cigarettes, and Their Co-Occurring Use: Disentangling Differences in Gray Matter Volume

BACKGROUND

Structural magnetic resonance imaging techniques are powerful tools for examining the effects of drug use on the brain. The nicotine and cannabis literature has demonstrated differences between nicotine cigarette smokers and cannabis users compared to controls in brain structure; however, less is known about the effects of co-occurring cannabis and tobacco use.

METHODS

We used voxel-based morphometry to examine gray matter volume differences between four groups: (1) cannabis-dependent individuals who do not smoke tobacco (Cs); (2) cannabis-dependent individuals who smoke tobacco (CTs); (3) cannabis-naïve, nicotine-dependent individuals who smoke tobacco (Ts); and (4) healthy controls (HCs). We also explored associations between gray matter volume and measures of cannabis and tobacco use.

RESULTS

A significant group effect was observed in the left putamen, thalamus, right precentral gyrus, and left cerebellum. Compared to HCs, the Cs, CTs, and Ts exhibited larger gray matter volumes in the left putamen. Cs also had larger gray matter volume than HCs in the right precentral gyrus. Cs and CTs exhibited smaller gray matter volume than HCs in the thalamus, and CTs and Ts had smaller left cerebellar gray matter volume than HCs.

CONCLUSIONS

This study extends previous research that independently examined the effects of cannabis or tobacco use on brain structure by including an examination of co-occurring cannabis and tobacco use, and provides evidence that cannabis and tobacco exposure are associated with alterations in brain regions associated with addiction.

Brain white matter microstructure in deficit and non-deficit subtypes of schizophrenia

Dividing schizophrenia into its deficit (SZD) and nondeficit (SZND) subtypes may help to identify specific and more homogeneous pathophysiological characteristics. Our aim was to define a whole brain voxelwise map specifically characterizing white matter tracts of schizophrenia patients with and without the deficit syndrome. We compared microstructural diffusion-related parameters as measured by diffusion tensor imaging in 21 SZD patients, 21 SZND patients, and 21 healthy controls, age- and gender-matched. Results showed that fractional anisotropy was reduced in the right precentral area in SZND patients, and in the left corona radiata of the schizophrenia group as a whole. Axial diffusivity was reduced in the left postcentral area of SZD patients and in the left cerebellum of the whole schizophrenia group. Radial diffusivity was increased in the left forceps minor of SZD patients, in the left internal capsule of SZND patients, and in the right inferior fronto-occipital fasciculus in the whole schizophrenia group. Mean diffusivity was increased from healthy controls to SZD patients to SZND patients in the right occipital lobe. In conclusion, SZD patients are not simply at the extreme end of a severity continuum of white matter disruption. Rather, the SZD and SZND subtypes are associated with distinct and specific brain microstructural anomalies that are consistent with their peculiar psychopathological dimensions.

The DTI connectivity of the human claustrum

The origin, structure, and function of the claustrum, as well as its role in neural computation, have remained a mystery since its discovery in the 17th century. Assessing the in vivo connectivity of the claustrum may bring forth useful insights with relevance to model the overall functionality of the claustrum itself. Using structural and diffusion tensor neuroimaging in N = 100 healthy subjects, we found that the claustrum has the highest connectivity in the brain by regional volume. Network theoretical analyses revealed that (a) the claustrum is a primary contributor to global brain network architecture, and that (b) significant connectivity dependencies exist between the claustrum, frontal lobe, and cingulate regions. These results illustrate that the claustrum is ideally located within the human central nervous system (CNS) connectome to serve as the putative "gate keeper" of neural information for consciousness awareness. Our findings support and underscore prior theoretical contributions about the involvement of the claustrum in higher cognitive function and its relevance in devastating neurological disease.

Static and dynamic characteristics of cerebral blood flow during the resting state in schizophrenia

BACKGROUND

The cerebral network that is active during rest and is deactivated during goal-oriented activity is called the default mode network (DMN). It appears to be involved in self-referential mental activity. Atypical functional connectivity in the DMN has been observed in schizophrenia. One hypothesis suggests that pathologically increased DMN connectivity in schizophrenia is linked with a main symptom of psychosis, namely, misattribution of thoughts.

METHODS

A resting-state pseudocontinuous arterial spin labeling (ASL) study was conducted to measure absolute cerebral blood flow (CBF) in 34 schizophrenia patients and 27 healthy controls. Using independent component analysis (ICA), the DMN was extracted from ASL data. Mean CBF and DMN connectivity were compared between groups using a 2-sample t test.

RESULTS

Schizophrenia patients showed decreased mean CBF in the frontal and temporal regions (P < .001). ICA demonstrated significantly increased DMN connectivity in the precuneus (x/y/z = -16/-64/38) in patients than in controls (P < .001). CBF was not elevated in the respective regions. DMN connectivity in the precuneus was significantly correlated with the Positive and Negative Syndrome Scale scores (P < .01).

CONCLUSIONS

In schizophrenia patients, the posterior hub--which is considered the strongest part of the DMN--showed increased DMN connectivity. We hypothesize that this increase hinders the deactivation of the DMN and, thus, the translation of cognitive processes from an internal to an external focus. This might explain symptoms related to defective self-monitoring, such as auditory verbal hallucinations or ego disturbances.

Structural and functional aspects relating to cost and benefit of rich club organization in the human cerebral cortex

Recent findings have demonstrated that a small set of highly connected brain regions may play a central role in enabling efficient communication between cortical regions, together forming a densely interconnected "rich club." However, the density and spatial layout of the rich club also suggest that it constitutes a costly feature of brain architecture. Here, combining anatomical T1, diffusion tensor imaging, magnetic transfer imaging, and functional MRI, several aspects of structural and functional connectivity of the brain's rich club were examined. Our findings suggest that rich club regions and rich club connections exhibit high levels of wiring volume, high levels of white matter organization, high levels of metabolic energy usage, long maturational trajectories, more variable regional time series, and more inter-regional functional couplings. Taken together, these structural and functional measures extend the notion that rich club organization represents a high-cost feature of brain architecture that puts a significant strain on brain resources. The high cost of the rich club may, however, be offset by significant functional benefits that the rich club confers to the brain network as a whole.

Network hubs in the human brain

Virtually all domains of cognitive function require the integration of distributed neural activity. Network analysis of human brain connectivity has consistently identified sets of regions that are critically important for enabling efficient neuronal signaling and communication. The central embedding of these candidate 'brain hubs' in anatomical networks supports their diverse functional roles across a broad range of cognitive tasks and widespread dynamic coupling within and across functional networks. The high level of centrality of brain hubs also renders them points of vulnerability that are susceptible to disconnection and dysfunction in brain disorders. Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function.

Slow gait speed - an indicator of lower cerebral vasoreactivity in type 2 diabetes mellitus

OBJECTIVE

Gait speed is an important predictor of health that is negatively affected by aging and type 2 diabetes. Diabetes has been linked to reduced vasoreactivity, i.e., the capacity to regulate cerebral blood flow in response to CO2 challenges. This study aimed to determine the relationship between cerebral vasoreactivity and gait speed in older adults with and without diabetes.

RESEARCH DESIGN AND METHODS

We studied 61 adults with diabetes (65 ± 8 years) and 67 without diabetes (67 ± 9 years) but with similar distribution of cardiovascular risk factors. Preferred gait speed was calculated from a 75 m walk. Global and regional perfusion, vasoreactivity and vasodilation reserve were measured using 3-D continuous arterial spin labeling MRI at 3 Tesla during normo-, hyper- and hypocapnia and normalized for end-tidal CO2.

RESULTS

Diabetic participants had slower gait speed as compared to non-diabetic participants (1.05 ± 0.15 m/s vs. 1.14 ± 0.14 m/s, p < 0.001). Lower global vasoreactivity (r (2) adj = 0.13, p = 0.007), or lower global vasodilation reserve (r (2) adj = 0.33, p < 0.001), was associated with slower walking in the diabetic group independently of age, BMI and hematocrit concentration. For every 1 mL/100 g/min/mmHg less vasodilation reserve, for example, gait speed was 0.05 m/s slower. Similar relationships between vasodilation reserve and gait speed were also observed regionally within the cerebellum, frontal, temporal, parietal, and occipital lobes (r (2) adj = 0.27-0.33, p < 0.0001). In contrast, vasoreactivity outcomes were not associated with walking speed in non-diabetic participants, despite similar vasoreactivity ranges across groups.

CONCLUSION

In the diabetic group only, lower global vasoreactivity was associated with slower walking speed. Slower walking in older diabetic adults may thus hallmark reduced vasomotor reserve and thus the inability to increase perfusion in response to greater metabolic demands during walking.

Constructing the resting state structural connectome

Background: Many recent studies have separately investigated functional and white matter (WM) based structural connectivity, yet their relationship remains less understood. In this paper, we proposed the functional-by-structural hierarchical (FSH) mapping to integrate multimodal connectome data from resting state fMRI (rsfMRI) and the whole brain tractography-derived connectome.

Methods: FSH first observes that the level of resting-state functional correlation between any two regions in general decreases as the graph distance of the corresponding structural connectivity matrix between them increases. As not all white matter tracts are actively in use (i.e., “utilized”) during resting state, FSH thus models the rsfMRI correlation as an exponential decay function of the graph distance of the rsfMRI-informed structural connectivity or rsSC. rsSC is mathematically computed by multiplying entry-by-entry the tractography-derived structural connectivity matrix with a binary white matter “utilization matrix” U. U thus encodes whether any specific WM tract is being utilized during rsFMRI, and is estimated using simulated annealing. We applied this technique and investigated the hierarchical modular structure of rsSC from 7 depressed subjects and 7 age/gender matched controls.

Results: No significant group differences were detected in the modular structures of either the resting state functional connectome or the whole brain tractography-derived connectome. By contrast, FSH results revealed significantly different patterns of association in the bilateral posterior cingulate cortex and right precuneus, with the depressed group exhibiting stronger associations among regions instrumental in self-referential operations.

Discussion: The results of this study support that enhanced sensitivity can be obtained by integrating multimodal imaging data using FSH, a novel computational technique that may increase power to detect group differences in brain connectomes.

Stress and neurodevelopmental processes in the emergence of psychosis

The notion that stress plays a role in the etiology of psychotic disorders, especially schizophrenia, is longstanding. However, it is only in recent years that the potential neural mechanisms mediating this effect have come into sharper focus. The introduction of more sophisticated models of the interplay between psychosocial factors and brain function has expanded our opportunities for conceptualizing more detailed psychobiological models of stress in psychosis. Further, scientific advances in our understanding of adolescent brain development have shed light on a pivotal question that has challenged researchers; namely, why the first episode of psychosis typically occurs in late adolescence/young adulthood. In this paper, we begin by reviewing the evidence supporting associations between psychosocial stress and psychosis in diagnosed patients as well as individuals at clinical high risk for psychosis. We then discuss biological stress systems and examine changes that precede and follow psychosis onset. Next, research findings on structural and functional brain characteristics associated with psychosis are presented; these findings suggest that normal adolescent neuromaturational processes may go awry, thereby setting the stage for the emergence of psychotic syndromes. Finally, a model of neural mechanisms underlying the pathogenesis of psychosis is presented and directions for future research strategies are explored.

Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain

Human brain functional networks contain a few densely connected hubs that play a vital role in transferring information across regions during resting and task states. However, the relationship of these functional hubs to measures of brain physiology, such as regional cerebral blood flow (rCBF), remains incompletely understood. Here, we used functional MRI data of blood-oxygenation-level-dependent and arterial-spin-labeling perfusion contrasts to investigate the relationship between functional connectivity strength (FCS) and rCBF during resting and an N-back working-memory task. During resting state, functional brain hubs with higher FCS were identified, primarily in the default-mode, insula, and visual regions. The FCS showed a striking spatial correlation with rCBF, and the correlation was stronger in the default-mode network (DMN; including medial frontal-parietal cortices) and executive control network (ECN; including lateral frontal-parietal cortices) compared with visual and sensorimotor networks. Moreover, the relationship was connection-distance dependent; i.e., rCBF correlated stronger with long-range hubs than short-range ones. It is notable that several DMN and ECN regions exhibited higher rCBF per unit connectivity strength (rCBF/FCS ratio); whereas, this index was lower in posterior visual areas. During the working-memory experiment, both FCS-rCBF coupling and rCBF/FCS ratio were modulated by task load in the ECN and/or DMN regions. Finally, task-induced changes of FCS and rCBF in the lateral-parietal lobe positively correlated with behavioral performance. Together, our results indicate a tight coupling between blood supply and brain functional topology during rest and its modulation in response to task demands, which may shed light on the physiological basis of human brain functional connectome.

Studying the topological organization of the cerebral blood flow fluctuations in resting state

In this paper the cerebral blood flow (CBF) in resting state obtained from SPECT imaging is employed as a hemodynamics descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in CBF between pairs of regions was measured by computing the Pearson correlation coefficient across 31 normal subjects. We demonstrated the CBF connectivity matrices follow 'small-world' attributes similar to previous studies using different modalities of neuroimaging data (MRI, fMRI, EEG, MEG). The highest concurrent fluctuations in CBF were detected between homologous cortical regions (homologous callosal connections). It was found that the existence of structural core regions or hubs positioned on a high proportion of shortest paths within the CBF network. These were anatomically distributed in frontal, limbic, occipital and parietal regions that suggest its important role in functional integration. Our findings point to a new possibility of using CBF variable to investigate the brain networks based on graph theory in normal and pathological states. Likewise, it opens a window to future studies to link covariation between morphometric descriptors, axonal connectivity and CBF processes with a potential diagnosis applications.

Resting State Changes in Functional Connectivity Correlate With Movement Recovery for BCI and Robot-Assisted Upper-Extremity Training After Stroke

Background. Robot-assisted training may improve motor function in some hemiparetic patients after stroke, but no physiological predictor of rehabilitation progress is reliable. Resting state functional magnetic resonance imaging (RS-fMRI) may serve as a method to assess and predict changes in the motor network. Objective. The authors examined the effects of upper-extremity robot-assisted rehabilitation (MANUS) versus an electroencephalography-based brain computer interface setup with motor imagery (MI EEG-BCI) and compared pretreatment and posttreatment RS-fMRI. Methods. In all, 9 adults with upper-extremity paresis were trained for 4 weeks with a MANUS shoulder-elbow robotic rehabilitation paradigm. In 3 participants, robot-assisted movement began if no voluntary movement was initiated within 2 s. In 6 participants, MI-BCI–based movement was initiated if motor imagery was detected. RS-fMRI and Fugl-Meyer (FM) upper-extremity motor score were assessed before and after training. Results. The individual gain in FM scores over 12 weeks could be predicted from functional connectivity changes (FCCs) based on the pre-post differences in RS-fMRI measurements. Both the FM gain and FCC were numerically higher in the MI-BCI group. Increases in FC of the supplementary motor area, the contralesional and ipsilesional motor cortex, and parts of the visuospatial system with mostly association cortex regions and the cerebellum correlated with individual upper-extremity function improvement. Conclusion. FCC may predict the steepness of individual motor gains. Future training could therefore focus on directly inducing these beneficial increases in FC. Evaluation of the treatment groups suggests that MI is a potential facilitator of such neuroplasticity.

Hierarchical structural mapping for globally optimized estimation of functional networks

In this study, we propose a framework to map functional MRI (fMRI) activation signals using DTI-tractography. This framework, which we term functional by structural hierarchical (FSH) mapping, models the regional origin of fMRI brain activation to construct "N-step reachable structural maps". Linear combinations of these N-step reachable maps are then used to predict the observed fMRI signals. Additionally, we constructed a utilization matrix, which numerically estimates whether the inclusion of a specific structural connection better predicts fMRI, using simulated annealing. We applied this framework to a visual fMRI task in a sample of body dysmorphic disorder (BDD) subjects and comparable healthy controls. Group differences were inferred by comparing the observed utilization differences against 10,000 permutations under the null hypothesis. Results revealed that BDD subjects under-utilized several key local connections in the visual system, which may help explain previously reported fMRI findings and further elucidate the underlying pathophysiology of BDD.

Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex

Cortical thickness correlation across individuals has been observed. So far, it remains unclear to what extent such a correlation in thickness is a reflection of underlying fiber connection. Here we explicitly compared the patterns of cortical thickness correlation and diffusion-based fiber connection across the entire cerebral cortex, in 95 normal adults. Interregional thickness correlations were extracted by using computational neuroanatomy algorithms based on structural MRI, and diffusion connections were detected by using diffusion probabilistic tractography. Approximately 35-40% of thickness correlations showed convergent diffusion connections across the cerebral cortex. Intriguingly, the observed convergences between thickness correlation and diffusion connection are mostly focused on the positive thickness correlations, while almost all of the negative correlations (>90%) did not have a matched diffusion connection, suggesting different mechanisms behind the positive and negative thickness correlations, the latter not being mediated by a direct fiber pathway. Furthermore, graph theoretic analysis reveals that the thickness correlation network has a more randomized overall topology, whereas the nodal characteristics of cortical regions in these two networks are statistically correlated. These findings indicate that thickness correlations partly reflect underlying fiber connections but they contains exclusive information, and therefore should not be simply taken as a proxy measure for fiber connections.