Individual differences in brain metrics, especially connectivity measured with functional MRI, can correlate with differences in motion during data collection. The assumption has been that motion causes artifactual differences in brain connectivity that must and can be corrected. Here we propose that differences in brain connectivity can also represent a neurobiological trait that predisposes to differences in motion. We support this possibility with an analysis of intra-versus intersubject differences in connectivity comparing high-to low-motion subgroups. Intersubject analysis identified a correlate of head motion consisting of reduced distant functional connectivity primarily in the default network in individuals with high head motion. Similar connectivity differences were not found in analysis of intrasubject data. Instead, this correlate of head motion was a stable property in individuals across time. These findings suggest that motion-associated differences in brain connectivity cannot fully be attributed to motion artifacts but rather also reflect individual variability in functional organization.resting-state fMRI | motion correction | connectome H ead motion has long been known as a confounding factor in brain imaging including MRI (1, 2), PET (3, 4), singlephoton emission computerized tomography (5, 6), and near infrared spectroscopy (7), but has raised particular concerns recently following the growing prominence of resting-state functional connectivity MRI. Studies found that head motion can vary considerably across individuals and often demonstrates systematic group effects when contrasting different populations, especially in neurodevelopmental (8-10), aging (11, 12), and neuropsychiatric studies (13). Some recent work reported that head motion augmented local coupling of the blood oxygenation level-dependent (BOLD) signal but reduced distant coupling (14-16). These correlations between connectivity measures and head motion have raised appropriate concern that previously observed differences in connectivity are due to artifact induced by differences in head motion. For example, developmental changes in functional connectivity might also be predicted by head motion (15). The assumption has been that head motion causes distorted connectivity measurements that must be addressed through improved motion-correction techniques (15). However, this correlation could be driven by causal factors in the other direction. Specifically, individual differences in brain connectivity could determine how well a subject can lie still in the scanner. This is not unreasonable as individual differences in structural connectivity can predict trait anxiety and can be related to attention deficits (17, 18) and individual differences in resting-state functional MRI (fMRI) measures may relate to various behavioral differences, including impulsivity (19)(20)(21)(22). In such a scenario, certain intersubject differences in connectivity measures could persist even after the most rigorous motion correction, as has been suggested ...

Epileptic spike is an indicator of hyper-excitability and hyper-synchrony of neural networks. While cognitive deficit in epilepsy is a common observation, how spikes transiently influence brain oscillations, especially those essential for cognitive functions, remains obscure. Here we aimed to quantify the transient impacts of sporadic spikes on theta oscillations and investigate how such impacts may evolve during epileptogenesis. Longitudinal depth EEG data were recorded in the CA1 area of pilocarpine temporal lobe epilepsy (TLE) rat models. Phase stability, a measure of synchrony, and theta power were estimated around spikes as well as in the protracted spike-free periods (FP) at least one hour after spike bursts. We found that the change in theta power did not correlate with the change in phase stability. More importantly, the impact of spikes on theta rhythm was highly time-dependent. While theta power decreased abruptly after spikes both in the latent and chronic stages, changes of theta phase stability demonstrated opposite trends in the latent and chronic stages, potentially due to the substantial reorganization of neural circuits along epileptogenesis. During FP, theta phase stability was significantly higher than the baseline level before injections, indicating that hyper-synchrony remained even hours after the spike bursts. We concluded that spikes have transient negative effects on theta rhythm, however, impacts are different during latent and chronic stages, implying that its influence on cognitive processes may also change over time during epileptogenesis.

Interictal spike is a biomarker of epilepsy that can occur frequently between seizures. Its potential effects on brain oscillations, especially on theta rhythm (4-8 Hz) that is related to a variety of cognitive processes, remain controversial. Using local field potentials recorded from patients with temporal lobe epilepsy (TLE), we investigated here the impact of spikes on theta rhythm immediately after spikes and during the prolonged periods (lasting 4-36 s) between adjacent spikes. Local field potentials (LFPs) were recorded in different epileptogenic areas including the anterior hippocampus (aH) and the entorhinal cortex (EC) as well as in the extended propagation pathway. We found that interictal spikes had a significant inhibitory effect on theta rhythm. Power of theta rhythm was reduced immediately after spikes, and the inhibitory effect on theta rhythm might sustain during the prolonged between-spike periods. The inhibitory effect was more severe when the epileptogenic areas involved both the aH and EC compared to that involved only a single structure. These observations suggest that interictal spikes have a significant negative impact on theta rhythm and may thus play a role in theta-related cognition changes in patients with TLE.

Noninvasive localization of brain function is used to understand and treat neurological disease, exemplified by pre-operative fMRI mapping prior to neurosurgical intervention. The principal approach for generating these maps relies on brain responses evoked by a task and, despite known limitations, has dominated clinical practice for over 20 years. Recently, pre-operative fMRI mapping based on correlations in spontaneous brain activity has been demonstrated, however this approach has its own limitations and has not seen widespread clinical use. Here we show that spontaneous and task-based mapping can be performed together using the same pre-operative fMRI data, provide complimentary information relevant for functional localization, and can be combined to improve identification of eloquent motor cortex. Accuracy, sensitivity, and specificity of our approach are quantified through comparison with electrical cortical stimulation mapping in eight patients with intractable epilepsy. Broad applicability and reproducibility of our approach is demonstrated through prospective replication in an independent dataset of six patients from a different center. In both cohorts and every individual patient, we see a significant improvement in signal to noise and mapping accuracy independent of threshold, quantified using receiver operating characteristic curves. Collectively, our results suggest that modifying the processing of fMRI data to incorporate both task-based and spontaneous activity significantly improves functional localization in pre-operative patients. Because this method requires no additional scan time or modification to conventional pre-operative data acquisition protocols it could have widespread utility.

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