A quantitative analysis of spontaneous alternation behaviors on a Y-maze reveals adverse effects of acute social isolation on spatial working memory.

Animals tend to alternate between different choices, which requires the ability to remember recent choices. The Y-maze spontaneous alternation test is widely used in various animal models for assessing short-term memory, and its precise evaluation depends upon the accurate determination of the arm visit sequence. However, an objective method for defining arm visits is lacking owing to uncertainty regarding the extent to which an animal must go into the arm to be considered visited. Here, we conducted quantitative analyses on mice behavior in the Y-maze while systematically varying the arm visit threshold and assessed the effect of acute social isolation on spatial working memory. Our results revealed that 24-h social isolation significantly reduced spontaneous alternation rate when the arm threshold was set at the distal part of the arm. Furthermore, the memory of the recently visited arms faded away faster in the socially isolated mice. However, other behavioral factors were comparable to those of the group-housed mice, indicating a specific impairment of short-term memory. Our findings suggest that the location of arm visit threshold is critical for the precise evaluation of short-term memory, and our study provides a method for comprehensively and systematically assessing spontaneous alternation behavior in the Y-maze.

Nonuniformity of Whole-Cerebral Neural Resource Allocation, a Neuromarker of the Broad-Task Attention.

The neural basis of attention is thought to involve the allocation of limited neural resources. However, the quantitative validation of this hypothesis remains challenging. Here, we provide quantitative evidence that the nonuniform allocation of neural resources across the whole cerebral gray matter reflects the broad-task process of sustained attention. We propose a neural measure for the nonuniformity of whole-cerebral allocation using functional magnetic resonance imaging. We found that this measure was significantly correlated with conventional indicators of attention level, such as task difficulty and pupil dilation. We further found that the broad-task neural correlates of the measure belong to frontoparietal and dorsal attention networks. Finally, we found that patients with attention-deficit/hyperactivity disorder showed abnormal decreases in the level of the proposed measure, reflecting the executive dysfunction. This study proposes a neuromarker suggesting that the nonuniform allocation of neural resources may be the broad-task neural basis of sustained attention.

Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study.

Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over time, not the brain-state transition itself. Here, we propose a novel dynamic functional connectivity analysis method, brain-state extraction algorithm based on state transition (BEST), which constructs connectivity matrices from the duration of brain-states and decodes the proper number of brain-states in a data-driven way. To set the duration of each brain-state, we detected brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time. Furthermore, we also used Bayesian information criterion to the clustering method to estimate and extract the number of brain-states. Through validations, it was proved that BEST could find brain-state transition time-points and could estimate the proper number of brain-states without any a priori knowledge. It has also shown that BEST can be applied to resting state fMRI data and provide stable and consistent results.

Altered brain function in persistent postural perceptual dizziness: A study on resting state functional connectivity.

This study used resting state functional magnetic resonance imaging (rsfMRI) to investigate whole brain networks in patients with persistent postural perceptual dizziness (PPPD). We compared rsfMRI data from 38 patients with PPPD and 38 healthy controls using whole brain and region of interest analyses. We examined correlations among connectivity and clinical variables and tested the ability of a machine learning algorithm to classify subjects using rsfMRI results. Patients with PPPD showed: (a) increased connectivity of subcallosal cortex with left superior lateral occipital cortex and left middle frontal gyrus, (b) decreased connectivity of left hippocampus with bilateral central opercular cortices, left posterior opercular cortex, right insular cortex and cerebellum, and (c) decreased connectivity between right nucleus accumbens and anterior left temporal fusiform cortex. After controlling for anxiety and depression as covariates, patients with PPPD still showed decreased connectivity between left hippocampus and right inferior frontal gyrus, bilateral temporal lobes, bilateral insular cortices, bilateral central opercular cortex, left parietal opercular cortex, bilateral occipital lobes and cerebellum (bilateral lobules VI and V, and left I-IV). Dizziness handicap, anxiety, and depression correlated with connectivity in clinically meaningful brain regions. The machine learning algorithm correctly classified patients and controls with a sensitivity of 78.4%, specificity of 76.9%, and area under the curve = 0.88 using 11 connectivity parameters. Patients with PPPD showed reduced connectivity among the areas involved in multisensory vestibular processing and spatial cognition, but increased connectivity in networks linking visual and emotional processing. Connectivity patterns may become an imaging biomarker of PPPD.

Momentary level of slow default mode network activity is associated with distinct propagation and connectivity patterns in the anesthetized mouse cortex.

Complex spatiotemporal changes of slow spontaneous activity occur in the form of propagating waves in the cortex, leading to the transient formation of a specific activation topography, followed by a transition in the topography. The topographies resemble the stimulation-induced activation patterns and the underlying structural projections, suggesting that they contain motifs of task-related activation. However, little is known about how propagation-mediated transitions between topographies are structured in terms of functional connectivity. Therefore, we investigated whether specific topographies or regions are associated with transitions involving long-range connections and hub modulation. We hypothesized that the activity level of the default mode network (DMN) at a given topography would affect the pattern of upcoming transitions, since high activity levels of the DMN are a distinct feature of the brain at rest. Using mesoscale voltage-sensitive dye imaging in the cortex of lightly anesthetized mice, we revealed that momentary levels of DMN activity are associated with distinct patterns of activity propagation and functional connectivity. High levels of DMN activity led to activity propagation across secondary and association cortices, increasing the centrality of a main hub region, whereas low-level activity led to global, diffuse, yet efficient changes in functional connectivity. Furthermore, low levels of activity resulted in increased long-range connectivity between frontal and posterior regions of the cortex. Our results indicate that DMN activity is associated with functional connectivity and wave propagation patterns, raising the possibility that the DMN may be involved in the modulation of long-range information processing associated with upcoming transitions. NEW & NOTEWORTHY Using voltage-sensitive dye imaging with high spatiotemporal resolution, we have revealed that increased DMN activity is associated with activity propagation to secondary/association cortices, whereas decreased activity is associated with stronger long-range frontal-posterior connections in the mouse cortex. Hub metric and global functional connectivity parameters were accompanied by activity level changes. These results indicate that the DMN may aid in modulating the structure of transitions.

Default Mode Network Functional Connectivity in Early and Late Mild Cognitive Impairment: Results From the Alzheimer's Disease Neuroimaging Initiative.

BACKGROUND: Default mode network (DMN) functional connectivity is one of the neuroimaging candidate biomarkers of Alzheimer disease. However, no studies have investigated DMN connectivity at different stages of mild cognitive impairment (MCI). The aim of this study was to investigate patterns of DMN connectivity and its breakdown among cognitively normal (CN), early MCI (EMCI), and late MCI (LMCI) subjects. METHODS: Magnetic resonance imaging data and neuropsychological test scores from 130 subjects (CN=43, EMCI=47, LMCI=40) were obtained from the Alzheimer's Disease Neuroimaging Initiative. DMN functional connectivity was extracted using independent components analysis and compared between groups. RESULTS: Functional connectivity in the precuneus, bilateral medial frontal, parahippocampal, middle temporal, right superior temporal, and left angular gyri was decreased in EMCI subjects compared with CN subjects. When the 2 MCI groups were directly compared, LMCI subjects exhibited decreased functional connectivity in the precuneus, bilateral medial frontal gyri, and left angular gyrus. There was no significant difference in gray matter volume among the 3 groups. Amyloid-positive EMCI subjects revealed more widespread breakdown of DMN connectivity than amyloid-negative EMCI subjects. A quantitative index of DMN connectivity correlated well with measures of cognitive performance. CONCLUSIONS: Our results suggest that the breakdown of DMN connectivity may occur in the early stage of MCI.

Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease.

Influence of ROI selection on resting state functional connectivity: an individualized approach for resting state fMRI analysis.

The differences in how our brain is connected are often thought to reflect the differences in our individual personalities and cognitive abilities. Individual differences in brain connectivity has long been recognized in the neuroscience community however it has yet to manifest itself in the methodology of resting state analysis. This is evident as previous studies use the same region of interest (ROIs) for all subjects. In this paper we demonstrate that the use of ROIs which are standardized across individuals leads to inaccurate calculations of functional connectivity. We also show that this problem can be addressed by taking an individualized approach by using subject-specific ROIs. Finally we show that ROI selection can affect the way we interpret our data by showing different changes in functional connectivity with aging.

Neural substrates of motor and non-motor symptoms in Parkinson's disease: a resting FMRI study.

BACKGROUND: Recently, non-motor symptoms of Parkinson's disease (PD) have been considered crucial factors in determining a patient's quality of life and have been proposed as the predominant features of the premotor phase. Researchers have investigated the relationship between non-motor symptoms and the motor laterality; however, this relationship remains disputed. This study investigated the neural connectivity correlates of non-motor and motor symptoms of PD with respect to motor laterality. METHODS: Eight-seven patients with PD were recruited and classified into left-more-affected PD (n = 44) and right-more affected PD (n = 37) based on their MDS-UPDRS (Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale) motor examination scores. The patients underwent MRI scanning, which included resting fMRI. Brain regions were labeled as ipsilateral and contralateral to the more-affected body side. Correlation analysis between the functional connectivity across brain regions and the scores of various symptoms was performed to identify the neural connectivity correlates of each symptom. RESULTS: The resting functional connectivity centered on the ipsilateral inferior orbito-frontal area was negatively correlated with the severity of non-motor symptoms, and the connectivity of the contralateral inferior parietal area was positively correlated with the severity of motor symptoms (p < 0.001, |r| > 0.3). CONCLUSIONS: These results suggest that the inferior orbito-frontal area may play a crucial role in non-motor dysfunctions, and that the connectivity information may be utilized as a neuroimaging biomarker for the early diagnosis of PD.