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Kirsch, H. E.

Publications and source records attributed to Kirsch, H. E..

3 recordsLinked to original sources

Neurophysiological trajectories in Alzheimer's disease progression

Alzheimers disease (AD) is characterized by the accumulation of amyloid-{beta} and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

neuroscience↗

Whole brain network analysis of neural synchrony and information flow during transition from wakefulness to light non-rapid eye movement sleep

Sleep is a highly stereotyped phenomenon, requiring robust spatial and temporal coordination of neural activity. How the brain coordinates neural activity with sleep onset can provide insight into the physiological functions subserved by sleep and pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wake to non-rapid eye movement (NREM) sleep using magnetoencephalography imaging in healthy subjects. In addition, we performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The sleep transition was identified to be encoded in spatially and temporally specific patterns of local and long-range neural synchrony. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset. Finally, biophysical neural mass modeling demonstrated spatially heterogeneous properties of cortical excitation-to-inhibition from wake to NREM. Together, these findings reveal whole-brain corticocortical structure in the sleep-wake transition and demonstrate the orchestration of local and long-range, frequency-specific cortical interactions that are fundamental to sleep onset.

bioengineering↗

Direct evidence for prediction signals in frontal cortex independent of prediction error

Predictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction-error signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from prediction-error signals has been challenging. Critically, while many studies found indirect evidence for predictive coding in the form of prediction-error signals, direct evidence for the prediction signal is mostly lacking. Here we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex generates prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The amplitude of high frequency broadband (HFB) neural activity was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single trial HFB amplitudes predicted deviations and correlated with post-stimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of prediction-error signals.

neuroscience↗