Biology subjects

Dahmani, L.

Publications and source records attributed to Dahmani, L..

4 recordsLinked to original sources

bioRxiv2024

Individualized functional connectivity markers associated with motor and mood symptoms of Parkinson's disease

Parkinsons disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. Using functional MRI, we extensively sampled 166 patients with PD (64 women, 102 men; mean age=61.8 years, SD=7.81) and 51 healthy control participants (32 women, 19 men; mean age=55.68 years, SD=7.62). We found that a model consisting of 44 functional connections predicted both motor (UPDRS-III: Pearson r=0.21, FDR-adjusted p=0.006) and mood symptoms (HAMD: Pearson r=0.23, FDR-adjusted p=0.006; HAMA: Pearson r=0.21, FDR-adjusted p=0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in two independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.

neuroscience↗

bioRxiv2024

Individual connectivity-based parcellations reflect functional properties of human auditory cortex

Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of auditory cortex. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.

neuroscience↗

bioRxiv2023

The somato-cognitive action network links diverse neuromodulatory targets for Parkinson's disease

The newly-recognized somato-cognitive action network (SCAN) is posited to be important in movement coordination. Functional disruptions in Parkinsons disease (PD) correspond with complex, non-effector-specific motor symptoms, indicating that SCAN dysfunction may underlie these symptoms. Along the same lines, the SCAN may link multiple neuromodulatory targets used for PD treatment. To investigate the role of the SCAN in PD, we leveraged resting-state precision functional mapping, analyzing data from 673 individuals across 6 independent datasets and 5 types of neuromodulation. Our findings revealed functional abnormalities within the SCAN in PD patients and the selective involvement of the SCAN in diverse neuromodulatory targets. Moreover, our data suggests causal links between SCAN connectivity changes and motor symptom alleviation following both invasive and non-invasive neuromodulation. Collectively, these findings underscore the critical role of the SCAN in the pathophysiology of PD and its brain stimulation treatments, and suggest the SCAN as a promising candidate target for neuromodulation.

neuroscience↗

bioRxiv2019

Reconstructing lost BOLD signal in individual participants using deep machine learning

The blood oxygen level-dependent (BOLD) signal in functional neuroimaging suffers from magnetic susceptibility artifacts and interference from metal implants. The resulting signal loss hampers functional neuroimaging studies and can lead to misinterpretation of findings. Here, we reconstructed compromised BOLD signal using deep machine learning. We trained a deep learning model to learn principles governing BOLD activity in one dataset and reconstructed artificially-compromised regions in another dataset, frame by frame. Strikingly, BOLD time series extracted from reconstructed frames were correlated with the original time series, even though the frames did not independently carry information about BOLD fluctuations through time. Moreover, reconstructed functional connectivity (FC) maps exhibited good correspondence with the original FC maps, indicating that the deep learning model recovered functional relationships among brain regions. We replicated this result in patients whose scans suffered signal loss due to intracortical electrodes. Critically, the reconstructions captured individual-specific information rather than group information learned during training. Deep machine learning thus presents a unique opportunity to reconstruct compromised BOLD signal while capturing features of an individuals own functional brain organization.

neuroscience↗