An anatomical and connectivity atlas of the marmoset cerebellum.

The cerebellum is essential for motor control and cognitive functioning, engaging in bidirectional communication with the cerebral cortex. The common marmoset, a small non-human primate, offers unique advantages for studying cerebello-cerebral circuits. However, the marmoset cerebellum is not well described in published resources. In this study, we present a comprehensive atlas of the marmoset cerebellum comprising (1) fine-detailed anatomical atlases and surface-analysis tools of the cerebellar cortex based on ultra-high-resolution ex vivo MRI, (2) functional connectivity and gradient patterns of the cerebellar cortex revealed by awake resting-state fMRI, and (3) structural-connectivity mapping of cerebellar nuclei using high-resolution diffusion MRI tractography. The atlas elucidates the anatomical details of the marmoset cerebellum, reveals distinct gradient patterns of intra-cerebellar and cerebello-cerebral functional connectivity, and maps the topological relationship of cerebellar nuclei in cerebello-cerebral circuits. As version 5 of the Marmoset Brain Mapping project, this atlas is publicly available at https://marmosetbrainmapping.org/MBMv5.html.

Mapping brain-wide excitatory projectome of primate prefrontal cortex at submicron resolution and comparison with diffusion tractography.

Resolving trajectories of axonal pathways in the primate prefrontal cortex remains crucial to gain insights into higher-order processes of cognition and emotion, which requires a comprehensive map of axonal projections linking demarcated subdivisions of prefrontal cortex and the rest of brain. Here, we report a mesoscale excitatory projectome issued from the ventrolateral prefrontal cortex (vlPFC) to the entire macaque brain by using viral-based genetic axonal tracing in tandem with high-throughput serial two-photon tomography, which demonstrated prominent monosynaptic projections to other prefrontal areas, temporal, limbic, and subcortical areas, relatively weak projections to parietal and insular regions but no projections directly to the occipital lobe. In a common 3D space, we quantitatively validated an atlas of diffusion tractography-derived vlPFC connections with correlative green fluorescent protein-labeled axonal tracing, and observed generally good agreement except a major difference in the posterior projections of inferior fronto-occipital fasciculus. These findings raise an intriguing question as to how neural information passes along long-range association fiber bundles in macaque brains, and call for the caution of using diffusion tractography to map the wiring diagram of brain circuits.

In the brain is a web of interconnected nerve cells that send messages to one another via spindly projections called axons. These axons join together at junctions called synapses to create circuits of nerve cells which connect neighboring or distant brain regions. Notably, long-range neural connections underpin higher-order cognitive skills (such as planning and emotion regulation) which make humans distinct from our primate relatives. Only by untangling these far-reaching networks can researchers begin to delineate what sets the human brain apart from other species. Researchers deploy a range of imaging techniques to map neural networks: scanning entire brains using MRI machines, or imaging thin slices of fluorescently labelled brain tissue using powerful microscopes. However, tracing long-range axons at a high resolution is challenging, and has stirred up debate about whether some neural tracts, such as the inferior fronto-occipital fasciculus, are present in all primates or only humans. To address these discrepancies, Yan, Yu et al. employed a two-pronged approach to map neural circuits in the brains of macaques. First, two techniques ­ called viral tracing and two-photon microscopy ­ were used to create a three-dimensional, fine-grain map showing how the ventrolateral prefrontal cortex (vlPFC), which regulates complex behaviors, connects to the rest of the brain. This revealed prominent axons from the vlPFC projecting via a single synapse to distant brain regions involved in higher-order functions, such as encoding memories and processing emotion. However, there were no direct, monosynaptic connections between the vlPFC and the occipital lobe, the brain's visual processing center at the back of the head. Next, Yan, Yu et al. used a specialized MRI scanner to create an atlas of neural circuits connected to the vlPFC, and compared these results to a technique tracing axons stained with a fluorescent dye. In general, there was good agreement between the two methods, except for major differences in the rear-end projections that typically form the inferior fronto-occipital fasciculus. This suggests that this long-range neural pathway exists in monkeys, but it connects via multiple synapses instead of a single junction as was previously thought. The findings of Yan, Yu et al. provide new insights on the far-reaching neural pathways connecting distant parts of the macaque brain. It also suggests that atlases of neural circuits from whole brain scans should be taken with caution and validated using neural tracing experiments.

The Contribution of White Matter Diffusion and Cortical Perfusion Pathology to Vascular Cognitive Impairment: A Multimode Imaging-Based Machine Learning Study.

Widespread impairments in white matter and cerebrovascular integrity have been consistently implicated in the pathophysiology of patients with small vessel disease (SVD). However, the neural circuit mechanisms that underlie the developing progress of clinical cognitive symptoms remain largely elusive. Here, we conducted cross-modal MRI scanning including diffusion tensor imaging and arterial spin labeling in a cohort of 113 patients with SVD, which included 74 patients with vascular mild cognitive impairment (vMCI) and 39 patients without vMCI symptoms, and hence developed multimode imaging-based machine learning models to identify markers that discriminated SVD subtypes. Diffusion and perfusion features, respectively, extracted from individual white matter and gray matter regions were used to train three sets of classifiers in a nested 10-fold fashion: diffusion-based, perfusion-based, and combined diffusion-perfusion-based classifiers. We found that the diffusion-perfusion combined classifier achieved the highest accuracy of 72.57% with leave-one-out cross-validation, with the diffusion features largely spanning the capsular lateral pathway of the cholinergic tracts, and the perfusion features mainly distributed in the frontal-subcortical-limbic areas. Furthermore, diffusion-based features within vMCI group were associated with performance on executive function tests. We demonstrated the superior accuracy of using diffusion-perfusion combined multimode imaging features for classifying vMCI subtype out of a cohort of patients with SVD. Disruption of white matter integrity might play a critical role in the progression of cognitive impairment in patients with SVD, while malregulation of coritcal perfusion needs further study.

Reversal of Functional Brain Activity Related to Gut Microbiome and Hormones After VSG Surgery in Patients With Obesity.

CONTEXT: Vertical sleeve gastrectomy (VSG) is becoming a prioritized surgical intervention for obese individuals; however, the brain circuits that mediate its effective control of food intake and predict surgical outcome remain largely unclear. OBJECTIVE: We investigated VSG-correlated alterations of the gut-brain axis. METHODS: In this observational cohort study, 80 patients with obesity were screened. A total of 36 patients together with 26 normal-weight subjects were enrolled and evaluated using the 21-item Three-Factor Eating Questionnaire (TFEQ), MRI scanning, plasma intestinal hormone analysis, and fecal sample sequencing. Thirty-two patients underwent VSG treatment and 19 subjects completed an average of 4-month follow-up evaluation. Data-driven regional homogeneity (ReHo) coupled with seed-based connectivity analysis were used to quantify VSG-related brain activity. Longitudinal alterations of body weight, eating behavior, brain activity, gastrointestinal hormones, and gut microbiota were detected and subjected to repeated measures correlation analysis. RESULTS: VSG induced significant functional changes in the right putamen (PUT.R) and left supplementary motor area, both of which correlated with weight loss and TFEQ scores. Moreover, postprandial levels of active glucagon-like peptide-1 (aGLP-1) and Ghrelin were associated with ReHo of PUT.R; meanwhile, relative abundance of Clostridia increased by VSG was associated with improvements in aGLP-1 secretion, PUT.R activity, and weight loss. Importantly, VSG normalized excessive functional connectivities with PUT.R, among which baseline connectivity between PUT.R and right orbitofrontal cortex was related to postoperative weight loss. CONCLUSION: VSG causes correlated alterations of gut-brain axis, including Clostridia, postprandial aGLP-1, PUT.R activity, and eating habits. Preoperative connectivity of PUT.R may represent a potential predictive marker of surgical outcome in patients with obesity.

Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model.

OBJECTIVE: Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used noninvasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity. METHODS: Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD: Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112; ABIDE-II, N=1,114; ADHD-200 sample: N=776; OCD local institutional database: N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders. RESULTS: Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively. CONCLUSIONS: The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.

A Fine-Tuned Positioning Algorithm for Space-Borne GNSS Timing Receivers.

To maximize the usage of limited transmission power and wireless spectrum, more communication satellites are adopting precise space-ground beam-forming, which poses a rigorous positioning and timing requirement of the satellite. To fulfill this requirement, a space-borne global navigation satellite system (GNSS) timing receiver with a disciplined high-performance clock is preferable. The space-borne GNSS timing receiver moves with the satellite, in contrast to its stationary counterpart on ground, making it tricky in its positioning algorithm design. Despite abundant existing positioning algorithms, there is a lack of dedicated work that systematically describes the delicate aspects of a space-borne GNSS timing receiver. Based on the experimental work of the LING QIAO (NORAD ID:40136) communication satellite's GNSS receiver, we propose a fine-tuned positioning algorithm for space-borne GNSS timing receivers. Specifically, the proposed algorithm includes: (1) a filtering architecture that separates the estimation of satellite position and velocity from other unknowns, which allows for a first estimation of satellite position and velocity incorporating any variation of orbit dynamics; (2) a two-threshold robust cubature Kalman filter to counteract the adverse influence of measurement outliers on positioning quality; (3) Reynolds averaging inspired clock and frequency error estimation. Hardware emulation test results show that the proposed algorithm has a performance with a 3D positioning RMS error of 1.2 m, 3D velocity RMS error of 0.02 m/s and a pulse per second (PPS) RMS error of 11.8ns. Simulations with MATLAB show that it can effectively detect and dispose outliers, and further on outperforms other algorithms in comparison.

MECP2 Duplication Causes Aberrant GABA Pathways, Circuits and Behaviors in Transgenic Monkeys: Neural Mappings to Patients with Autism.

MECP2 gain-of-function and loss-of-function in genetically engineered monkeys recapitulates typical phenotypes in patients with autism, yet where MECP2 mutation affects the monkey brain and whether/how it relates to autism pathology remain unknown. Here we report a combination of gene-circuit-behavior analyses including MECP2 coexpression network, locomotive and cognitive behaviors, and EEG and fMRI findings in 5 MECP2 overexpressed monkeys (Macaca fascicularis; 3 females) and 20 wild-type monkeys (Macaca fascicularis; 11 females). Whole-genome expression analysis revealed MECP2 coexpressed genes significantly enriched in GABA-related signaling pathways, whereby reduced ß-synchronization within fronto-parieto-occipital networks was associated with abnormal locomotive behaviors. Meanwhile, MECP2-induced hyperconnectivity in prefrontal and cingulate networks accounted for regressive deficits in reversal learning tasks. Furthermore, we stratified a cohort of 49 patients with autism and 72 healthy controls of 1112 subjects using functional connectivity patterns, and identified dysconnectivity profiles similar to those in monkeys. By establishing a circuit-based construct link between genetically defined models and stratified patients, these results pave new avenues to deconstruct clinical heterogeneity and advance accurate diagnosis in psychiatric disorders.SIGNIFICANCE STATEMENT Autism spectrum disorder (ASD) is a complex disorder with co-occurring symptoms caused by multiple genetic variations and brain circuit abnormalities. To dissect the gene-circuit-behavior causal chain underlying ASD, animal models are established by manipulating causative genes such as MECP2 However, it is unknown whether such models have captured any circuit-level pathology in ASD patients, as demonstrated by human brain imaging studies. Here, we use transgenic macaques to examine the causal effect of MECP2 overexpression on gene coexpression, brain circuits, and behaviors. For the first time, we demonstrate that the circuit abnormalities linked to MECP2 and autism-like traits in the monkeys can be mapped to a homogeneous ASD subgroup, thereby offering a new strategy to deconstruct clinical heterogeneity in ASD.

Intrinsic connectivity identifies the sensory-motor network as a main cross-network between remitted late-life depression- and amnestic mild cognitive impairment-targeted networks.

Remitted late-life depression (rLLD) and amnestic mild cognitive impairment (aMCI) are both associated with a high risk of developing Alzheimer's disease (AD). Neurodegeneration is considered to spread within pre-existing networks. To investigate whether, in the healthy brain, there was a pre-existing cross-network between the intrinsic networks that are vulnerable to rLLD and aMCI. We performed functional connectivity analyses based on brain areas with the greatest brain neuronal activity differences in 55 rLLD, 87 aMCI, and 114 healthy controls. Intrinsic networks that were differentially vulnerable to rLLD and aMCI converged onto the sensory-motor network (SMN) in the healthy brain. These regions in the SMN within the aMCI- and rLLD-vulnerable networks played different roles in the cognitive functions. This study identifies the SMN as a cross-network between rLLD- and aMCI-vulnerable networks. The common susceptibility of these diseases to AD is likely due to the breakdown of the cross-network. The results further suggest that interventions targeting the amelioration of sensory-motor deficits in the early course of disease in individuals with AD risk may enhance patient function as AD pathology progresses.

A graph representation of functional diversity of brain regions.

INTRODUCTION: Modern network science techniques are popularly used to characterize the functional organization of the brain. A major challenge in network neuroscience is to understand how functional characteristics and topological architecture are related in the brain. Previous task-based functional neuroimaging studies have uncovered a core set of brain regions (e.g., frontal and parietal) supporting diverse cognitive tasks. However, the graph representation of functional diversity of brain regions remains to be understood. METHODS: Here, we present a novel graph measure, the neighbor dispersion index, to test the hypothesis that the functional diversity of a brain region is embodied by the topological dissimilarity of its immediate neighbors in the large-scale functional brain network. RESULTS: We consistently identified in two independent and publicly accessible resting-state functional magnetic resonance imaging datasets that brain regions in the frontoparietal and salience networks showed higher neighbor dispersion index, whereas those in the visual, auditory, and sensorimotor networks showed lower neighbor dispersion index. Moreover, we observed that human fluid intelligence was associated with the neighbor dispersion index of dorsolateral prefrontal cortex, while no such association for the other metrics commonly used for characterizing network hubs was noticed even with an uncorrected p < .05. CONCLUSIONS: This newly developed graph theoretical method offers fresh insight into the topological organization of functional brain networks and also sheds light on individual differences in human intelligence.

BRANT: A Versatile and Extendable Resting-State fMRI Toolkit.

Data processing toolboxes for resting-state functional MRI (rs-fMRI) have provided us with a variety of functions and user friendly graphic user interfaces (GUIs). However, many toolboxes only cover a certain range of functions, and use exclusively designed GUIs. To facilitate data processing and alleviate the burden of manually drawing GUIs, we have developed a versatile and extendable MATLAB-based toolbox, BRANT (BRAinNetome fmri Toolkit), with a wide range of rs-fMRI data processing functions and code-generated GUIs. During the implementation, we have also empowered the toolbox with parallel computing techniques, efficient file handling methods for compressed file format, and one-line scripting. In BRANT, users can find rs-fMRI batch processing functions for preprocessing, brain spontaneous activity analysis, functional connectivity analysis, complex network analysis, statistical analysis, and results visualization, while developers can quickly publish scripts with code-generated GUIs.

Brain atrophy in middle-aged subjects with Type 2 diabetes mellitus, with and without microvascular complications.

BACKGROUND: The rapid rise in Type 2 diabetes mellitus (T2DM) among young adults makes it important to understand structural changes in the brain at a presenile stage. This study examined global and regional brain atrophy in middle-aged adults with T2DM, with a focus on those without clinical evidence of microvascular complications. METHODS: The study recruited 66 dementia-free middle-aged subjects (40 with T2DM, 26 healthy volunteers [HVs]). Patients were grouped according to the presence (T2DM-C; n = 20) or absence (T2DM-NC; n = 20) of diabetic microvascular complications. Global brain volume (including gray matter [GM] and white matter) was calculated based on voxel-based morphometry analysis. Regional GM volumes were further extracted using the anatomical automatic labeling template. RESULTS: There was a significant difference in global brain volume among groups (P = 0.003, anova). Global brain volume was lower in T2DM-C patients than in both T2DM-NC patients and HVs (mean [±SD] 0.720 ± 0.024 vs 0.736 ± 0.021 and 0.743 ± 0.019, respectively; P = 0.032 and P = 0.001, respectively). Regional analysis showed significant GM atrophy in the right Rolandic operculum (t = 3.42, P = 0.001) and right superior temporal gyrus (t = 2.803, P = 0.007) in T2DM-NC patients compared with age- and sex-matched HVs. CONCLUSIONS: Brain atrophy is present in dementia-free middle-aged adults with T2DM. Regional brain atrophy appears to be developing even in those with no clinical evidence of microvascular disturbances. The brain seems to be particularly vulnerable to metabolic disorders prior to peripheral microvascular pathologies associated with other target organs.

Aberrant Functional Connectivity Architecture in Participants with Chronic Insomnia Disorder Accompanying Cognitive Dysfunction: A Whole-Brain, Data-Driven Analysis.

Objectives: Although it is widely observed that chronic insomnia disorder (CID) is associated with cognitive impairment, the neurobiological mechanisms underlying this remain unclear. Prior neuroimaging studies have confirmed that a close correlation exists between functional connectivity and cognitive impairment. Based on this observation, in this study we used resting-state functional magnetic resonance imaging (rs-fMRI) to study the relationship between whole brain functional connectivity and cognitive function in CID. Methods: We included 39 patients with CID and 28 age-, gender-, and education-matched healthy controls (HC). Abnormalities in functional connectivity were identified by comparing the correlation coefficients for each pair of 116 brain regions between CID and HC. Results: Cognitive impairment was associated with reduced subjective insomnia scores after controlling for age, gender, and educational effects. Compared with HC, patients with CID had larger negative correlations within the task-negative network [medial prefrontal cortex (mPFC), precuneus, inferior temporal gyrus, cerebellum, and superior parietal gyrus], and between two intrinsic anti-correlation networks (mPFC and middle temporal gyrus; supplementary motor area and cerebellum). Patients with CID also had decreased positive correlations within the default mode network (DMN), and between the cerebellum and DMN, which mainly comprises the mPFC and posterior cingulated cortex. There were positive correlations of decreased positive connectivity with subjective sleep scores and MMSE scores, and increased negative correlations between the task-negative-network and MMSE scores in CID. Conclusions: Using rs-fMRI, our results support previous observations of cortical disconnection in CID in the prefrontal and DMN networks. Moreover, abnormal correlations within the task-negative network, and between two intrinsically anti-correlation networks, might be important neurobiological indicators of CID and associated cognitive impairment.

Microstructural Alterations in Asymptomatic and Symptomatic Patients with Spinocerebellar Ataxia Type 3: A Tract-Based Spatial Statistics Study.

OBJECTIVE: Spinocerebellar ataxia type 3 (SCA3) is the most commonly occurring type of autosomal dominant spinocerebellar ataxia. The present study aims to investigate progressive changes in white matter (WM) fiber in asymptomatic and symptomatic patients with SCA3. METHODS: A total of 62 participants were included in this study. Among them, 16 were asymptomatic mutation carriers (pre-SCA3), 22 were SCA3 patients with clinical symptoms, and 24 were normal controls (NC). Group comparison of tract-based spatial statistics was performed to identify microstructural abnormalities at different SCA3 disease stages. RESULTS: Decreased fractional anisotropy (FA) and increased mean diffusivity (MD) were found in the left inferior cerebellar peduncle and superior cerebellar peduncle (SCP) in the pre-SCA3 group compared with NC. The symptomatic SCA3 group showed brain-wide WM tracts impairment in both supratentorial and infratentorial networks, and the mean FA value of the WM skeleton showed a significantly negative correlation with the International Cooperative Ataxia Rating Scale (ICARS) scores. Specifically, FA of the bilateral posterior limb of the internal capsule negatively correlated with SCA3 disease duration. We also found that FA values in the right medial lemniscus and SCP negatively correlated with ICARS scores, whereas FA in the right posterior thalamic radiation positively correlated with Montreal Cognitive Assessment scores. In addition, MD in the middle cerebellar peduncle, left anterior limb of internal capsule, external capsule, and superior corona radiate positively correlated with ICARS scores in SCA3 patients. CONCLUSION: WM microstructural changes are present even in the asymptomatic stages of SCA3. In individuals in which the disease has progressed to the symptomatic stage, the integrity of WM fibers across the whole brain is affected. Furthermore, abnormalities in WM tracts are closely related to SCA3 disease severity, including movement disorder and cognitive dysfunction. These findings can deepen our understanding of the neural basis of SCA3 dysfunction.

Altered Intranetwork and Internetwork Functional Connectivity in Type 2 Diabetes Mellitus With and Without Cognitive Impairment.

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment. We investigated whether alterations of intranetwork and internetwork functional connectivity with T2DM progression exist, by using resting-state functional MRI. MRI data were analysed from 19 T2DM patients with normal cognition (DMCN) and 19 T2DM patients with cognitive impairment (DMCI), 19 healthy controls (HC). Functional connectivity among 36 previously well-defined brain regions which consisted of 5 resting-state network (RSN) systems [default mode network (DMN), dorsal attention network (DAN), control network (CON), salience network (SAL) and sensorimotor network (SMN)] was investigated at 3 levels (integrity, network and connectivity). Impaired intranetwork and internetwork connectivity were found in T2DM, especially in DMCI, on the basis of the three levels of analysis. The bilateral posterior cerebellum, the right insula, the DMN and the CON were mainly involved in these changes. The functional connectivity strength of specific brain architectures in T2DM was found to be associated with haemoglobin A1c (HbA1c), cognitive score and illness duration. These network alterations in intergroup differences, which were associated with brain functional impairment due to T2DM, indicate that network organizations might be potential biomarkers for predicting the clinical progression, evaluating the cognitive impairment, and further understanding the pathophysiology of T2DM.

Convergent and divergent intranetwork and internetwork connectivity patterns in patients with remitted late-life depression and amnestic mild cognitive impairment.

INTRODUCTION: Both remitted late-life depression (rLLD) and amnesiac mild cognitive impairment (aMCI) alter brain functions in specific regions of the brain. They are also disconnection syndromes that are associated with a high risk of developing Alzheimer's disease (AD). OBJECTIVES: Resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) was performed to define the shared and distinct aberrant patterns in intranetwork and internetwork connectivity between rLLD and aMCI and to determine how knowledge of these differences might contribute to our essential understanding of the altered sequences involved in functional systems both inside and outside of resting-state networks. METHODS: We used rs-fcMRI to investigate in five functionally well-defined brain networks in two large cohorts of subjects at high risk for AD (55 rLLD and 87 aMCI) and 114 healthy controls (HC). RESULTS: A reduced degree of functional connectivity was observed in the bilateral inferior temporal cortex and supplemental motor area, and reduced correlations were observed within the sensory-motor network (SMN) and in the default mode network (DMN)-control network (CON) pair in the rLLD group than the HC group. The aMCI group showed only focal functional changes in regions of interest pairs, a trend toward increased correlations within the salience network and SMN, and a trend toward a reduced correlation in the DMN-CON pair. Furthermore, the rLLD group exhibited more severely altered functional connectivity than the aMCI group. Interestingly, these altered connectivities were associated with specific multi-domain cognitive and behavioral functions in both rLLD and aMCI. The degree of functional connectivity in the right primary auditory areas was negatively correlated with Hamilton Depression Scale scores in rLLD. Notably, altered connectivity between the right middle temporal cortex and the posterior cerebellum was negatively correlated with Mattis Dementia Rating Scale scores in both rLLD and aMCI. CONCLUSIONS: These results demonstrate that rLLD and aMCI may share convergent and divergent aberrant intranetwork and internetwork connectivity patterns as a potential continuous spectrum of the same disease. They further suggest that dysfunctions in the right specific temporal-cerebellum neural circuit may contribute to the similarities observed in rLLD and aMCI conversion to AD.

Closed-form impulse response model of non-line-of-sight single-scatter propagation.

For optical scattering communication, a closed-form expression of channel impulse response (CIR) is favorable for further system design and channel capacity analysis. Combining the mean value theorem of integrals and L'Hôpital's rule, the exact non-line-of-sight (NLOS) single-scatter propagation model is simplified to a closed-form CIR model for a laser source with a narrow beam. Based on this model, by joint geometrical and empirical approaches, a piecewise CIR expression is presented under certain system NLOS geometries. Through numerical results on CIR for various NLOS geometries, the proposed model is verified with the exact NLOS single-scatter propagation model and the previous Gamma fitting model, showing that our model agrees better with the former than the latter.

Longitudinal Study of Impaired Intra- and Inter-Network Brain Connectivity in Subjects at High Risk for Alzheimer's Disease.

Alzheimer's disease (AD) is associated with abnormal resting-state network (RSN) architecture of the default mode network (DMN), the dorsal attention network (DAN), the executive control network (CON), the salience network (SAL), and the sensory-motor network (SMN). However, little is known about the disrupted intra- and inter-network architecture in mild cognitive impairment (MCI). Here, we employed a priori defined regions of interest to investigate the intra- and inter-network functional connectivity profiles of these RSNs in longitudinal participants, including normal controls (n = 23), participants with early MCI (n = 26), and participants with late MCI (n = 19). We found longitudinal alterations of functional connectivity within the DMN, where they were correlated with variation in cognitive ability. The SAL as well as the interaction between the DMN and the SAL were disrupted in MCI. Furthermore, our results demonstrate that longitudinal alterations of functional connectivity are more profound in earlier stages as opposed to later stages of the disease. The increased severity of cognitive impairment is associated with increasingly altered RSN connectivity patterns, suggesting that disruptions in functional connectivity may contribute to cognitive dysfunction and may represent a potential biomarker of impaired cognitive ability in MCI. Earlier prevention and treatment may help to delay disease progression to AD.

Aberrant intra- and inter-network connectivity architectures in Alzheimer's disease and mild cognitive impairment.

Alzheimer's disease (AD) patients and those with high-risk mild cognitive impairment are increasingly considered to have dysfunction syndromes. Large-scale network studies based on neuroimaging techniques may provide additional insight into AD pathophysiology. The aim of the present study is to evaluate the impaired network functional connectivity with the disease progression. For this purpose, we explored altered functional connectivities based on previously well-defined brain areas that comprise the five key functional systems [the default mode network (DMN), dorsal attention network (DAN), control network (CON), salience network (SAL), sensorimotor network (SMN)] in 35 with AD and 27 with mild cognitive impairment (MCI) subjects, compared with 27 normal cognitive subjects. Based on three levels of analysis, we found that intra- and inter-network connectivity were impaired in AD. Importantly, the interaction between the sensorimotor and attention functions was first attacked at the MCI stage and then extended to the key functional systems in the AD individuals. Lower cognitive ability (lower MMSE scores) was significantly associated with greater reductions in intra- and inter-network connectivity across all patient groups. These profiles indicate that aberrant intra- and inter-network dysfunctions might be potential biomarkers or predictors of AD progression and provide new insight into AD pathophysiology.

Aberrant Functional Connectivity Architecture in Alzheimer's Disease and Mild Cognitive Impairment: A Whole-Brain, Data-Driven Analysis.

The purpose of our study was to investigate whether the whole-brain functional connectivity pattern exhibits disease severity-related alterations in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Resting-state functional magnetic resonance imaging data were acquired in 27 MCI subjects, 35 AD patients, and 27 age- and gender-matched subjects with normal cognition (NC). Interregional functional connectivity was assessed based on a predefined template which parcellated the brain into 90 regions. Altered whole-brain functional connectivity patterns were identified via connectivity comparisons between the AD and NC subjects. Finally, the relationship between functional connectivity strength and cognitive ability according to the mini-mental state examination (MMSE) was evaluated in the MCI and AD groups. Compared with the NC group, the AD group exhibited decreased functional connectivities throughout the brain. The most significantly affected regions included several important nodes of the default mode network and the temporal lobe. Moreover, changes in functional connectivity strength exhibited significant associations with disease severity-related alterations in the AD and MCI groups. The present study provides novel evidence and will facilitate meta-analysis of whole-brain analyses in AD and MCI, which will be critical to better understand the neural basis of AD.