Zebrafish has been in the forefront of developmental biology and genetics, but only recently has interest in their behavior increased. Zebrafish are small and prolific, which lends this species to high throughput screening applications. A typical feature of zebrafish is its propensity to aggregate in groups, a behavior known as shoaling. Thus, zebrafish has been proposed as a possible model organism appropriate for the analysis of the genetics of vertebrate social behavior. However, shoaling behavior is not well characterized in zebrafish. Here, using a recently developed software application, we first investigate how zebrafish respond to conspecific and heterospecific fish species that differ in coloration and/or shoaling tendencies. We found that zebrafish shoaled with their own species but not with two heterospecific species, one of which was a shoaling the other a non-shoaling species. In addition, we have started the analysis of visual stimuli that zebrafish may utilize to determine whether to shoal with a fish or not. We systematically modified the color, the location, the pattern, and the body shape of computer animated zebrafish images and presented them to experimental zebrafish. The subjects responded differentially to some of these stimuli showing preference for yellow and avoidance of elongated zebrafish images. Our results suggest that computerized stimulus presentation and automated behavioral quantification of zebrafish responses are feasible, which in turn implies that high throughput forward genetic mutation or drug screening will be possible in the analysis of social behavior with this model organism.

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Recent work suggests that the default mode network (DMN) includes two core regions, the ventromedial prefrontal cortex and posterior cingulate cortex (PCC), and several unique subsystems that are functionally distinct. These include a medial temporal lobe (MTL) subsystem, active during remembering and future projection, and a dorsomedial prefrontal cortex (dmPFC) subsystem, active during self-reference. The PCC has been further subdivided into ventral (vPCC) and dorsal (dPCC) regions that are more strongly connected with the DMN and cognitive control networks, respectively. The goal of this study was to examine age differences in resting state functional connectivity within these subsystems. After applying a rigorous procedure to reduce the effects of head motion, we used a multivariate technique to identify both common and unique patterns of functional connectivity in the MTL vs. the dmPFC, and in vPCC vs. dPCC. All four areas had robust functional connectivity with other DMN regions, and each also showed distinct connectivity patterns in both age groups. Young and older adults had equivalent functional connectivity in the MTL subsystem. Older adults showed weaker connectivity in the vPCC and dmPFC subsystems, particularly with other DMN areas, but stronger connectivity than younger adults in the dPCC subsystem, which included areas involved in cognitive control. Our data provide evidence for distinct subsystems involving DMN nodes, which are maintained with age. Nevertheless, there are age differences in the strength of functional connectivity within these subsystems, supporting prior evidence that DMN connectivity is particularly vulnerable to age, whereas connectivity involving cognitive control regions is relatively maintained. These results suggest an age difference in the integrated activity among brain networks that can have implications for cognition in older adults.

Traumatic brain injury (TBI) is one of the leading causes of death and disability in North America and as such requires ongoing surveillance 1,2 . Tracking health resource utilization over time, by age, and by gender provides valuable information regarding the burden of TBI on health care services, including post acute care. Furthermore, accurately identifying the rates of TBI is critical to the planning and evaluation of prevention efforts.Recent reports based on hospital admission data in Canada and the United States have documented a decrease in the number of in-patient admissions over the last two decades 3,4 , particularly for children and for incidents of "mild" TBI (mTBI). Studies that focus on in-patient admissions, however, may be misleading in that the decrease in numbers could reflect a shift towards treating children and mTBI sufferers at emergency departments (EDs). To date, there are no recent peer reviewed studies documenting TBI-related ED visits at a population based level, in a publicly ABSTRACT: Objective: The aim of this study was to determine the number of annual hospitalizations and overall episodes of care that involve a traumatic brain injury (TBI) by age and gender in the province of Ontario. To provide a more accurate assessment of the prevalence of TBI, episodes of care included visits to the emergency department (ED), as well as admissions to hospital. Mechanisms of injury for overall episodes were also investigated. Methods: Traumatic brain injury cases from fiscal years 2002/03-2006/07 were identified by means of ICD-10 codes. Data were collected from the National Ambulatory Care Reporting System and the Discharge Abstract Database. Results: The rate of hospitalization was highest for elderly persons over 75 years-of-age. Males generally had higher rates for both hospitalizations and episodes of care than did females. The inclusion of ED visits to hospitalizations had the greatest impact on the rates of TBI in the youngest age groups. Episodes of care for TBI were greatest in youth under the age of 14 and elderly over the age of 85. Falls (41.6%) and being struck by or against an object (31.1%) were the most frequent causes for a TBI. Conclusions:The study provides estimates for TBI from the only Canadian province that has systematically captured ED visits in a national registry. It shows the importance of tracking ED visits, in addition to hospitalizations, to capture the burden of TBI on the health care system. Prevention strategies should include information on ED visits, particularly for those at younger ages. Cette étude fournit des estimés de LCT dans la seule province canadienne qui inscrit a systématiquement les visites à l'urgence dans un registre nationale. Elle montre l'importance de faire le suivi des visites à l'urgence en plus des hospitalisations pour apprécier le fardeau que constitue la LCT sur le système de santé. Les stratégies de prévention devraient mentionner l'information sur les visites à l'urgence, particulièrement chez les jeunes.

Rehospitalization after TBI is common. Factors associated with rehospitalization can inform long-term postdischarge planning. Findings also support examining causes for rehospitalization by age and sex.

Mild cognitive impairment (MCI) represents the intermediate stage between normal cerebral aging and dementia associated with Alzheimer's disease (AD). Early diagnosis of MCI and AD through artificial intelligence has captured considerable scholarly interest; researchers hope to develop therapies capable of slowing or halting these processes. We developed a state-of-the-art deep learning algorithm based on an optimized convolutional neural network (CNN) topology called MCADNNet that simultaneously recognizes MCI, AD, and normally aging brains in adults over the age of 75 years, using structural and functional magnetic resonance imaging (fMRI) data. Following highly detailed preprocessing, fourdimensional (4D) fMRI and 3D MRI were decomposed to create 2D images using a lossless transformation, which enables maximum preservation of data details. The samples were shuffled and subject-level training and testing datasets were completely independent. The optimized MCADNNet was trained and extracted invariant and hierarchical features through convolutional layers followed by multi-classification in the last layer using a softmax layer. A decision-making algorithm was also designed to stabilize the outcome of the trained models. To measure the performance of classification, the accuracy rates for various pipelines were calculated before and after applying the decision-making algorithm. Accuracy rates of 99.77% ± 0.36% and 97.5% ± 1.16% were achieved for MRI and fMRI pipelines, respectively, after applying the decisionmaking algorithm. In conclusion, a cutting-edge and optimized topology called MCADNNet was designed and preceded a preprocessing pipeline; this was followed by a decision-making step that yielded the highest performance achieved for simultaneous classification of the three cohorts examined. INDEX TERMS Deep learning, classification, structural and functional magnetic resonance imaging, brain, Alzheimer's disease, MCI. I. INTRODUCTION A. COGNITIVE IMPAIRMENT Cognitive impairment is a general term referring to impairments in cognition among the domains of memory, learning, The associate editor coordinating the review of this manuscript and approving it for publication was Mohan Venkateshkumar .

Current evidence suggests that two spatially distinct neuroanatomical networks, the dorsal attention network (DAN) and the default mode network (DMN), support externally and internally oriented cognition, respectively, and are functionally regulated by a third, frontoparietal control network (FPC). Interactions among these networks contribute to normal variations in cognitive functioning and to the aberrant affective profiles present in certain clinical conditions, such as major depression. Nevertheless, their links to non-clinical variations in affective functioning are still poorly understood. To address this issue, we used fMRI to measure the intrinsic functional interactions among these networks in a sample of predominantly younger women (N = 162) from the Human Connectome Project. Consistent with the previously documented dichotomous motivational orientations (i.e., withdrawal versus approach) associated with sadness versus anger, we hypothesized that greater sadness would predict greater DMN (rather than DAN) functional dominance, whereas greater anger would predict the opposite. Overall, there was evidence of greater DAN (rather than DMN) functional dominance, but this pattern was modulated by current experience of specific negative emotions, as well as subclinical depressive and anxiety symptoms. Thus, greater levels of currently experienced sadness and subclinical depression independently predicted weaker DAN functional dominance (i.e., weaker DAN-FPC functional connectivity), likely reflecting reduced goal-directed attention towards the external perceptual environment. Complementarily, greater levels of currently experienced anger and subclinical anxiety predicted greater DAN functional dominance (i.e., greater DAN-FPC functional connectivity and, for anxiety only, also weaker DMN-FPC coupling). Our findings suggest that distinct affective states Negative mood states foster internally oriented attention and perceptual decoupling from the here-and-now (Smallwood, O'Connor, Sudbery, & Obonsawin, 2007;Smallwood, Fitzgerald, Miles, & Phillips, 2009). Nevertheless, the neural signature and unique contribution of distinct negative emotions to this effect of mood on engagement with the external world have not been identified despite their significance to both normal and pathological variations in emotional functioning. To address this issue, the present research capitalized on existing evidence that the human brain is organized into dissociable anatomical networks (Fox & Raichle, 2007), which provide a latent functional architecture that is readily recruited during goal-directed cognition (Laird et al., 2011;Smith et al., 2009). Importantly, recent investigations have documented the key role that these intrinsic functional networks play in supporting not only cognitive, but also affective processes (i.e., emotion experience and perception, cf. Touroutoglou, Lindquist, Dickerson, & Barrett, in press), including those observed during experimentally induced variations in mood states (e.g., sadness, Harri...

Human aging is characterized by reductions in the ability to remember associations between items, despite intact memory for single items. Older adults also show less selectivity in task-related brain activity, such that patterns of activation become less distinct across multiple experimental tasks. This reduced selectivity or dedifferentiation has been found for episodic memory, which is often reduced in older adults, but not for semantic memory, which is maintained with age. We used fMRI to investigate whether there is a specific reduction in selectivity of brain activity during associative encoding in older adults, but not during item encoding, and whether this reduction predicts associative memory performance. Healthy young and older adults were scanned while performing an incidental encoding task for pictures of objects and houses under item or associative instructions. An old/new recognition test was administered outside the scanner. We used agnostic canonical variates analysis and split-half resampling to detect whole-brain patterns of activation that predicted item versus associative encoding for stimuli that were later correctly recognized. Older adults had poorer memory for associations than did younger adults, whereas item memory was comparable across groups. Associative encoding trials, but not item encoding trials, were predicted less successfully in older compared with young adults, indicating less distinct patterns of associative-related activity in the older group. Importantly, higher probability of predicting associative encoding trials was related to better associative memory after accounting for age and performance on a battery of neuropsychological tests. These results provide evidence that neural distinctiveness at encoding supports associative memory and that a specific reduction of selectivity in neural recruitment underlies age differences in associative memory.