Decoding neural activity in sulcal and white matter areas of the brain to accurately predict individual finger movement and tactile stimuli of the human hand

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions

Brain network signatures of depressive symptoms

Depressive symptoms are common in the general population. Even in individuals who do not meet the criteria for a Major Depression Disorder (MDD), their symptoms are of clinical relevance because they increase the likelihood of progressing into a full-blown depressive episode, which in turn increases the prevalence of future episodes. The studies in this thesis apply advanced computational methods to functional magnetic resonance imaging (fMRI) data to investigate the dynamics of network connectivity, with the aim of understanding what brain mechanisms make a person more vulnerable to depression. Our results suggest that imbalances in whole-brain connectivity can already be linked to higher levels of depressive symptoms in healthy (undiagnosed) individuals. These imbalances correspond to a reduced dynamism in the overall functional organization of the brain, suggesting a link between a ‘rigid brain’ and rigid behavior, such as the lack of flexibility in cognitive and emotional responses that often accompanies depressive symptoms. Additionally, individual differences in the repertoire of brain states indicate that people with more depressive symptoms engage more in states related to self-referential thinking. This tendency was also observed in remitted patients during the transition into a depressive episode. This emphasizes that the present experience of depressive symptoms, whether in healthy individuals or MDD patients, is associated with changes in brain communication. The findings of this thesis lead to a deeper understanding of the complex orchestration of brain communication and its changes concerning depressive symptomatology in clinical and nonclinical populations