Physical Activity and Cognitive Decline Among Older Adults: A Systematic Review and Meta-Analysis.

Importance: Physical activity is associated with the risk for cognitive decline, but much of the evidence in this domain comes from studies with short follow-ups, which is prone to reverse causation bias. Objective: To examine how length of follow-up, baseline age, physical activity amount, and study quality modify the longitudinal associations of physical activity with cognition. Data Sources: Observational studies of adults with a prospective follow-up of at least 1 year, a valid baseline cognitive measure or midlife cohort, and an estimate of the association of baseline physical activity and follow-up cognition were sought from PsycInfo, Scopus, CINAHL, Web of Science, SPORTDiscus, and PubMed, with the final search conducted on November 2, 2022. Study Selection: Two independent researchers screened titles with abstracts and full-text reports. Data Extraction and Synthesis: Two reviewers independently assessed study quality and extracted data. Pooled estimates of association were calculated with random-effects meta-analyses. An extensive set of moderators, funnel plots, and scatter plots of physical activity amount were examined. This study is reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Main Outcomes and Measures: Pooled estimates of the associations between physical activity and global cognition, as well as specific cognitive domains, were examined. Results: A total of 104 studies with 341 471 participants were assessed. Analysis of binary outcomes included 45 studies with 102 452 individuals, analysis of follow-up global cognition included 14 studies with 41 045 individuals, and analysis of change in global cognition included 25 studies with 67 463 individuals. Physical activity was associated with a decreased incidence of cognitive impairment or decline after correction for funnel plot asymmetry (pooled risk ratio, 0.97; 95% CI, 0.97-0.99), but there was no significant association in follow-ups longer than 10 years. Physical activity was associated with follow-up global cognition (standardized regression coefficient, 0.03; 95% CI, 0.02-0.03) and change in global cognition (standardized regression coefficient, 0.01; 95% CI, 0.01 to 0.02) from trim-and-fill analyses, with no clear dose-response or moderation by follow-up length, baseline age, study quality or adjustment for baseline cognition. The specific cognitive domains associated with physical activity were episodic memory (standardized regression coefficient, 0.03; 95% CI, 0.02-0.04) and verbal fluency (standardized regression coefficient, 0.05; 95% CI, 0.03-0.08). Conclusions and Relevance: In this meta-analysis of the association of physical activity with cognitive decline, physical activity was associated with better late-life cognition, but the association was weak. However, even a weak association is important from a population health perspective.

Convolutional neural networks for detection of transthyretin amyloidosis in 2D scintigraphy images.

BACKGROUND: Transthyretin amyloidosis (ATTR) is a progressive disease which can be diagnosed non-invasively using bone avid [99mTc]-labeled radiotracers. Thus, ATTR is also an occasional incidental finding on bone scintigraphy. In this study, we trained convolutional neural networks (CNN) to automatically detect and classify ATTR from scintigraphy images. The study population consisted of 1334 patients who underwent [99mTc]-labeled hydroxymethylene diphosphonate (HMDP) scintigraphy and were visually graded using Perugini grades (grades 0-3). A total of 47 patients had visual grade ≥ 2 which was considered positive for ATTR. Two custom-made CNN architectures were trained to discriminate between the four Perugini grades of cardiac uptake. The classification performance was compared to four state-of-the-art CNN models. RESULTS: Our CNN models performed better than, or equally well as, the state-of-the-art models in detection and classification of cardiac uptake. Both models achieved area under the curve (AUC) ≥ 0.85 in the four-class Perugini grade classification. Accuracy was good in detection of negative vs. positive ATTR patients (grade < 2 vs grade ≥ 2, AUC > 0.88) and high-grade cardiac uptake vs. other patients (grade < 3 vs. grade 3, AUC = 0.94). Maximum activation maps demonstrated that the automated deep learning models were focused on detecting the myocardium and not extracardiac features. CONCLUSION: Automated convolutional neural networks can accurately detect and classify different grades of cardiac uptake on bone scintigraphy. The CNN models are focused on clinically relevant image features. Automated screening of bone scintigraphy images using CNN could improve the early diagnosis of ATTR.

The effect of visual and proprioceptive feedback on sensorimotor rhythms during BCI training.

Brain-computer interfaces (BCI) can be designed with several feedback modalities. To promote appropriate brain plasticity in therapeutic applications, the feedback should guide the user to elicit the desired brain activity and preferably be similar to the imagined action. In this study, we employed magnetoencephalography (MEG) to measure neurophysiological changes in healthy subjects performing motor imagery (MI) -based BCI training with two different feedback modalities. The MI-BCI task used in this study lasted 40-60 min and involved imagery of right- or left-hand movements. 8 subjects performed the task with visual and 14 subjects with proprioceptive feedback. We analysed power changes across the session at multiple frequencies in the range of 4-40 Hz with a generalized linear model to find those frequencies at which the power increased significantly during training. In addition, the power increase was analysed for each gradiometer, separately for alpha (8-13 Hz), beta (14-30 Hz) and gamma (30-40 Hz) bands, to find channels showing significant linear power increase over the session. These analyses were applied during three different conditions: rest, preparation, and MI. Visual feedback enhanced the amplitude of mainly high beta and gamma bands (24-40 Hz) in all conditions in occipital and left temporal channels. During proprioceptive feedback, in contrast, power increased mainly in alpha and beta bands. The alpha-band enhancement was found in multiple parietal, occipital, and temporal channels in all conditions, whereas the beta-band increase occurred during rest and preparation mainly in the parieto-occipital region and during MI in the parietal channels above hand motor regions. Our results show that BCI training with proprioceptive feedback increases the power of sensorimotor rhythms in the motor cortex, whereas visual feedback causes mainly a gamma-band increase in the visual cortex. MI-BCIs should involve proprioceptive feedback to facilitate plasticity in the motor cortex.

Adaptive neural network classifier for decoding MEG signals.

We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain-computer interfaces (BCI).

Publisher Correction: Across-subject offline decoding of motor imagery from MEG and EEG.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Across-subject offline decoding of motor imagery from MEG and EEG.

Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects' data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG. Six methods were tested on data involving MEG and EEG measurements of healthy participants. Inter-subject decoders were trained on subjects showing good within-subject accuracy, and tested on all subjects, including poor performers. Three methods were based on Common Spatial Patterns (CSP), and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using (1) MI and (2) passive movements (PM) for training, separately for MEG and EEG. With MI training, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEG yielded slightly better average accuracies than EEG. With PM training, none of the inter-subject methods yielded above chance level (58.7%) accuracy. In conclusion, MTL and training with other subject's MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers.

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).

Comparing Features for Classification of MEG Responses to Motor Imagery.

BACKGROUND: Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. METHODS: MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. RESULTS: The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. CONCLUSIONS: We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction methods utilizing both the spatial and spectral profile of MI-related signals provided the best classification results, suggesting good performance of these methods in an online MEG neurofeedback system.

Neural mechanisms supporting evaluation of others' errors in real-life like conditions.

The ability to evaluate others' errors makes it possible to learn from their mistakes without the need for first-hand trial-and-error experiences. Here, we compared functional magnetic resonance imaging activation to self-committed errors during a computer game to a variety of errors committed by others during movie clips (e.g., figure skaters falling down and persons behaving inappropriately). While viewing errors by others there was activation in lateral and medial temporal lobe structures, posterior cingulate cortex, precuneus, and medial prefrontal cortex possibly reflecting simulation and storing for future use alternative action sequences that could have led to successful behaviors. During both self- and other-committed errors activation was seen in the striatum, temporoparietal junction, and inferior frontal gyrus. These areas may be components of a generic error processing mechanism. The ecological validity of the stimuli seemed to matter, since we largely failed to see activations when subjects observed errors by another player in the computer game, as opposed to observing errors in the rich real-life like human behaviors depicted in the movie clips.