Baseline characteristics by group. by Carol Kotliar (22351525)

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Clinical covariates and cognitive response phenotypes. by Carol Kotliar (22351525)

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Data underlying the findings. by Carol Kotliar (22351525)

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CONSORT flow diagram of participant recruitment and retention. by Carol Kotliar (22351525)

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Effect of MTT 24.5 program on cognitive abilities outcomes. by Carol Kotliar (22351525)

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A. Mean change from baseline on Cognitive Ability Scores. by Carol Kotliar (22351525)

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MTT’s structural unit defined as the combination of a new knowledge and cognitive skills for its effective learning and memorization. by Carol Kotliar (22351525)

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Right Knee fNIRS MI Dataset by Hammad Gilani (8060012)

“…<br></li></ul><p dir="ltr">This dataset can be used to explore neural signatures of lower limb motor imagery, develop brain-computer interface (BCI) algorithms, and investigate cortical hemodynamics associated with motor planning and control.…”

Left Knee fNIRS MI Dataset by Hammad Gilani (8060012)

“…<br></li></ul><p dir="ltr">This dataset can be used to explore neural signatures of lower limb motor imagery, develop brain-computer interface (BCI) algorithms, and investigate cortical hemodynamics associated with motor planning and control.…”

Left Ankle fNIRS MI Dataset by Hammad Gilani (8060012)

“…<br></li></ul><p dir="ltr">This dataset can be used to explore neural signatures of lower limb motor imagery, develop brain-computer interface (BCI) algorithms, and investigate cortical hemodynamics associated with motor planning and control.…”

Both Knees fNIRS MI dataset by Hammad Gilani (8060012)

“…<br></li></ul><p dir="ltr">This dataset can be used to explore neural signatures of lower limb motor imagery, develop brain-computer interface (BCI) algorithms, and investigate cortical hemodynamics associated with motor planning and control.…”

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”

Connectivity matrices attached in a zip file used to generate the results. by Caroline L. Alves (14271413)

S2 File - by Caroline L. Alves (14271413)

“…Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. …”

Computational efficiency of the DySCo framework. by Giuseppe de Alteriis (20846230)

Presentation of the DySCo framework. by Giuseppe de Alteriis (20846230)

“…<p>A: What is dynamic Functional Connectivity: i) We can start from any set of brain recordings, where each signal is referred to a brain location (e.g. fMRI, EEG, intracranial recordings in rodents, and more). ii) “Static” Functional Connectivity (FC) is a matrix where each entry is a time aggregated functional measure of interaction between two regions, for example, the Pearson Correlation Coefficient. iii) Dynamic Functional Connectivity (dFC) is a FC matrix (that can be calculated in different ways, see below) that changes with time, under the assumption that patterns of brain interactions are non-stationary. …”

Overview of MINT. by Gabriel Matías Lorenz (21094672)

“…<p>A: List of main MINT functions. B: MINT provides multivariate information theoretic functions to quantify the amount of information that single neurons or neural populations carry about task-relevant variables (e.g., sensory stimuli or behavioral choices). …”

Decoding evidence for feature triplets on test tasks. by Sam Hall-McMaster (10343795)

“…This figure shows average decoding evidence for features associated with the more and less rewarding training policies on test trials (y-axis) as a function of brain region (x-axis). Feature information could not be decoded above chance in the four brain regions of interest (corrected <i>p-</i>values > 0.05). …”

Workflow of the multi-modal synthesis network. by Yun Peng (464975)