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Background There is limited prospective data on the prevalence, timing of onset, and characteristics of acute headache following concussion/mild traumatic brain injury. Methods Adults diagnosed with concussion (arising from injuries not related to work or motor vehicle accidents) were recruited from emergency departments and seen within one week post injury wherein they completed questionnaires assessing demographic variables, pre-injury headache history, post-injury headache history, and the Sport Concussion Assessment Tool (SCAT-3) symptom checklist, the Sleep and Concussion Questionnaire (SCQ) and mood/anxiety on the Brief Symptom Inventory (BSI). Results A total of 302 participants (59% female) were enrolled (mean age 33.6 years) and almost all (92%) endorsed post-traumatic headache (PTH) with 94% endorsing headache onset within 24 hours of injury. Headache location was not correlated with site of injury. Most participants (84%) experienced daily headache. Headache quality was pressure/squeezing in 69% and throbbing/pulsing type in 22%. Associated symptoms included: photophobia (74%), phonophobia (72%) and nausea (55%). SCAT-3 symptom scores, Brief Symptom Inventory and Sleep and Concussion Questionnaire scores were significantly higher in those endorsing acute PTH. No significant differences were found in week 1 acute PTH by sex, history of migraine, pre-injury headache frequency, anxiety, or depression, nor presence/absence of post-traumatic amnesia and self-reported loss of consciousness. Conclusions This study highlights the very high incidence of acute PTH following concussion, the timing of onset and characteristics of acute PTH, the associated psychological and sleep disturbances and notes that the current ICHD-3 criteria for headaches attributed to mild traumatic injury to the head are reasonable, the interval between injury and headache onset should not be extended beyond seven days and could, potentially, be shorted to allow for greater diagnostic precision.

Objective To characterize the prevalence of, and relationship between, self-reported balance disturbance and performance-based balance impairment in the general population with concussion. Setting Rehabilitation hospital outpatient concussion clinic. Participants One hundred six individuals with concussion (49 males, mean age = 32.4; SD = 11.5 years), mean (SD) = 5 (1.8) days postinjury. Design Cross-sectional observational study. Main measures SCAT3 Symptoms Subscale, Balance Error Scoring System, modified Balance Error Scoring System, and center-of-pressure root-mean-square amplitude and velocity in eyes open and closed conditions. Results The majority of participants reported balance or dizziness symptoms (54% and 63%, respectively) and demonstrated balance impairment on the Balance Error Scoring System and modified Balance Error Scoring System (66% and 58%, respectively). The prevalence of balance impairment across center-of-pressure measures varied from 32% to 48%. There was no effect of balance or dizziness symptom severity on any of the balance measures (F2,103 = 1.02; P = .44) and (F2,103 = 1.45; P = .10), respectively. There was poor agreement between self-report of balance/dizziness symptoms and identified balance impairment across all balance measures (all κ ≤ 0.26 and κ ≤ 0.20, respectively). Conclusions There was no clear relationship between the subjective experience of balance disturbance and objective measures of balance impairment in the general population. These results have implications for clinical evaluation of postconcussive deficits and determination of recovery.

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, four-dimensional (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 decision-making 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.

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.

As the interest in functional connectivity continues to increase among neuroimaging researchers there becomes a greater need to develop an objective method of network identification. The current paper offers a solution to this problem by developing a robust decision making algorithm that can extract a target neural network from an array of spatial maps. We used a probabilistic independent component analysis to generate spatial maps of the Default Mode Network (DMN); however, this adaptive pipeline can be applied to any network of interest. Different template matching algorithms including: Normalized Cross-Correlation, Sum of Squared Differences and Dice Coefficient, were applied to the spatial and frequency domains of the dataset to identify the components that shared the greatest similarity to our DMN template. After identifying components within the resting state, the decision making pipeline selected the components within each method that had the highest matching scores to our DMN template. The final decision of selecting the most prototypical DMN components was made by a comparison between methods. This resulted in a DMN mask that was generated by the components chosen by our decision-making algorithm. To evaluate the accuracy of the decision-maker, a cross-correlation between each final mask and the template was measured. Results indicated that the Normalized Cross Correlation method, using both the spatial and frequency domain, and the Dice Coefficient method, generated the optimal DMN mask. This demonstrates the utility of our algorithm in providing an objective method for network extraction.

Older adults typically show weaker functional connectivity (FC) within brain networks compared with young adults, but stronger functional connections between networks. Our primary aim here was to use a graph theoretical approach to identify age differences in the FC of 3 networks—default mode network (DMN), dorsal attention network, and frontoparietal control (FPC)—during rest and task conditions and test the hypothesis that age differences in the FPC would influence age differences in the other networks, consistent with its role as a cognitive “switch.” At rest, older adults showed lower clustering values compared with the young, and both groups showed more between-network connections involving the FPC than the other 2 networks, but this difference was greater in the older adults. Connectivity within the DMN was reduced in older compared with younger adults. Consistent with our hypothesis, between-network connections of the FPC at rest predicted the age-related reduction in connectivity within the DMN. There was no age difference in within-network FC during the task (after removing the specific task effect), but between-network connections were greater in older adults than in young adults for the FPC and dorsal attention network. In addition, age reductions were found in almost all the graph metrics during the task condition, including clustering and modularity. Finally, age differences in between-network connectivity of the FPC during both rest and task predicted cognitive performance. These findings provide additional evidence of less within-network but greater between-network FC in older adults during rest but also show that these age differences can be altered by the residual influence of task demands on background connectivity. Our results also support a role for the FPC as the regulator of other brain networks in the service of cognition. Critically, the link between age differences in inter-network connections of the FPC and DMN connectivity, and the link between FPC connectivity and performance, support the hypothesis that FC of the FPC influences the expression of age differences in other networks, as well as differences in cognitive function.

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 and subclinical mood symptoms have dissociable neural signatures, reflective of the symbiotic relationship between cognitive processes and emotional states.

Objective To examine, from a Canadian population-based perspective, the incidence and etiology of long-term hospital utilization among persons living with traumatic brain injury (TBI) by age and sex. Design Retrospective cohort study. Setting Acute care hospitals. Participants Index cases of TBI (N=29,269) were identified from the Discharge Abstract Database for fiscal years 2002/2003 through 2009/2010 and were followed-up until 36 months after injury. Interventions Not applicable. Main Outcome Measures Rehospitalization was defined as admission to an acute care facility that occurred up to 36 months after index injury. Diagnoses associated with subsequent rehospitalization were examined by age and sex. Results Of the patients with TBI, 35.5% (n=10,390) were subsequently hospitalized during the 3-year follow-up period. Multivariable logistic regression (controlling for index admission hospital) identified men, older age, mechanism of injury being a fall, greater injury severity, rural residence, greater comorbidity, and psychiatric comorbidity to be significant predictors of rehospitalization in a 3-year period postinjury. The most common causes for rehospitalization differed by age and sex. Conclusions 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.

Older adults show a ‘positivity bias’ in tasks involving emotion and self-referential processing. A critical network that is involved in self-referencing and shows age-related decline is the default network (DN). The purpose of the current study was to investigate age differences in pre- and post-task DN functional connectivity (FC) and signal variability, and to examine whether they are predictive of the positivity bias in self-referencing. We measured FC and within-subject variability of the DN in resting-state scans preceding and following tasks involving personality judgements on the self and a close other. Older adults endorsed more positive traits than younger adults on both tasks. FC was weaker post-task in older vs younger adults, and younger adults had greater variability than older adults in DN nodes. Younger adults with higher post-task DN variability had more negative self-ratings. For both age groups, greater FC in the DN was associated with more negative self-ratings. Neither FC nor variability was related to other ratings, despite the potential for self-processing when making other judgements. Our findings suggest that ageing leads to reduced FC and variability in the DN, which is most apparent after task, and may be one mechanism underlying the positive bias with age.

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.