The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample t-tests were performed on the local minimums with Bonferroni correction to control the family-wise error rate. These local minimums are connectivity states showing not only which brain regions but also how strong they are working together. They work as aging signatures that can be used to differentiate young and old groups. We found that the attention network’s connectivity signature is a state with all the regions working together and young subjects have a stronger average connectivity among these regions. We have also found a common pattern between young and old subjects where the left and right brain regions of the frontal network are sometimes working separately instead of together. In summary, in this work, we combined machine learning and statistical approaches to extract connectivity signatures, which can be utilized to distinguish aging brains and monitor possible treatment efficacy.

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Computational neuroscience models can be used to understand neural dynamics in the brain and these dynamics change as the physiological and other conditions like aging. One such approach we have used in this work is Energy Landscape analysis based on resting-state fMRI data. The dataset consists of 70 subjects with normal cognitive function, of which 23 are young adults and 47 are old adults. In this analysis, disconnectivity graphs and activity patterns are generated and using connectivity statistics among seven prominent brain networks. To study brain dynamic behaviors, we perform sliding window studies on the dataset and observe local minima of each window evolving in time. By varying the window shift from multiple seconds to 1 second, we can obtain statistics and evaluate the speed and activity pattern holding time of individual and group subjects. We found that older subjects can hold the brain states for a longer time but then jump to other dominated brain state local minima with a large hamming distance, whereas young subjects change dominated local minima more frequently but with a small hamming distance of 1 or 2. In fact, when averaged over the full time course, old subjects have more stable brain states local minima compared to young subjects. For both young and old subjects, the default mode network (DMN) and visual network (VIS) are coupled but for young subjects the two networks are on and off together and strongly correlated. For old subjects, there is an extra dominated brain state local minimum that the DMN and attention network (ATN) are correlated and anti-correlated with (VIS) and sensory-motor networks (SMN). This state may suggest old subjects are more capable of focusing on brain internal models and not getting influenced by external visual and sensory factors than young subjects.

Segmenting the human brain into networks has been a useful approach in analyzing functional connectivity. Brain network bundling can determine which regions are engaged and if they are working together. The thalamus (THL) and basal ganglia (BSL) regions in the subcortical network are linked to multiple cortical areas due to their roles in neural circuitry outlined in the cortico-basal ganglia-thalamo cortical map. Here we explore their coupling with the default mode network (DMN), frontoparietal network (FPN), salience network (SAN), attention network (ATN), sensorimotor network (SSM), visual network (VIS), and auditory network (AUD) using the energy landscape technique. Energy landscape analysis helps identify the statistical differences in functional behaviors between the healthy control and patient groups, which are obtained from the fMRI activity time courses of the 9 internetworks. In this work, we focused on studying 107 schizophrenic patients and 86 healthy controls and obtained the constructed activity patterns and disconnectivity graphs of each subject. The differences between two groups are compared. The results from bundling THL and BSL with the DMN, FPN, SAN, ATN, SSM, VIS, and AUD shows that these regions are more strongly coupled in controls than in patients. After performing energy calculations and heat map generations, we observed several lower energy band states that are common among all control and patient subjects. The potential implications of these common band states are discussed.

The study of brain activity changes caused by physiological or other conditions like aging is crucial not only to understand the brain dynamics but also to identify those changes and distinguish the subject groups. In this work, we are performing a sliding window technique on the Energy Landscape analysis to explore temporal signatures of the seven major restingstate networks, namely, default mode (DMN), frontal-parietal (FPN), salience (SAN), attention (ATN), sensory-motor (SMN), visual (VIS) and auditory (AUD) networks. The dataset used for this study consists of 23 young adult and 47 old adult subjects with normal cognitive function. To study the dynamic behavior of the brain, we have applied the sliding window technique on the time courses of the obtained fMRI data. With 90-second windows and 4-second shifts from a total of 180 second time course, we obtain 24 windows of temporal energy landscape information, which is presented as a matrix with the energies of all possible connectivity states vs the sequence of sliding windows. A heat map was displayed using this matrix to examine the energy transition of these states. We found that a few bands of connectivity states are consistently low energies among the different groups of subjects. One observation was that the states in these bands are only one or two hamming distances away from each other, which means these connectivity states with consistently low energy values are close in terms of the region of interest (ROIs) connectivity. Also, SAN and ATN were working synchronously for both young and old subjects in all these bands. In summary, we are using the sliding window technique with the Energy landscape analysis to find out the brain state dynamics for the old and young subjects.