Person-specific and precision neuroimaging: Current methods and future directions

Michon, Katherine J.; Khammash, Dalia; Simmonite, Molly; Hamlin, Abbey M.; Polk, Thad A.

doi:10.1016/j.neuroimage.2022.119589

Cited by 34 publications

(23 citation statements)

References 135 publications

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“…Network variance found in fMRI studies are related to differences across individuals rather than transient factors like day-to-day fluctuations ( Gratton et al, 2018 , Poldrack et al, 2015 ), and a group description alone tends to fall short of adequately capturing functional brain activity at the level of precision needed to translate into a clinical impact ( Gratton et al, 2018 , Gratton et al, 2020 ). Differences between individual and group-average network descriptions carry important ramifications for analysis, as it is speculated that functional neuroimaging may not achieve its full potential until individual-level brain network estimates can be made ( Michon et al, 2022 , Gratton et al, 2022 ). Consequently, there is a need for a shift away from group aggregate averages and consider individual metrics that can better characterize variability.…”

Section: Introductionmentioning

confidence: 99%

Longer illness duration is associated with greater individual variability in functional brain activity in Schizophrenia, but not bipolar disorder

Gallucci

Pomarol‐Clotet

Voineskos

et al. 2022

NeuroImage: Clinical

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Section: Introductionmentioning

confidence: 99%

Longer illness duration is associated with greater individual variability in functional brain activity in Schizophrenia, but not bipolar disorder

Gallucci

Pomarol‐Clotet

Voineskos

et al. 2022

NeuroImage: Clinical

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“…This goal could be achieved by detecting patterns in multichannel EEG (e.g., (Ruch et al, 2022)), and/or machine learning-approaches that can detect fine-grained signatures on one or several channels that stimulate the brain with optimal timing (e.g., Valenchon et al (2022)), and perhaps adapt detection parameters in real time to an individual's idiosyncratic neural signals. These ideas are in alignment with the emerging concepts of 'precision neuroimaging' and 'person specific methods', in which reliable individual differences in brain activation or connectivity are studied to understand (and sometimes make use of) individual differences in brain function and behaviour Michon et al (2022).…”

Section: Discussionmentioning

confidence: 92%

The neurophysiology of closed-loop auditory stimulation in sleep: a magnetoencephalography study

Jourde

Merlo

Brooks

et al. 2022

Preprint

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Closed-loop auditory stimulation (CLAS) is a brain modulation technique in which sounds are timed to enhance or disrupt endogenous neurophysiological events. CLAS of slow oscillation up-states in sleep is becoming a popular tool to study and potentially enhance sleep's functions, as it can increase slow oscillations, evoke sleep spindles, and enhance memory consolidation of certain tasks. However, few studies have examined the specific neurophysiological mechanisms involved in CLAS, in part because of practical limitations to commonly-used tools. To evaluate evidence for possible models of how sound stimulation during brain up-states might generate slow oscillations, we simultaneously recorded electro- and magnetoencephalography in human participants who received auditory stimulation across sleep stages and neural oscillation phases. The results suggest that auditory information reaches ventral frontal lobe areas via non-lemniscal pathways. From there, a slow oscillation is created and propagated. We demonstrate that while the state of excitability of tissue in auditory cortex and frontal ventral regions shows some synchrony with the EEG-recorded up-states that are commonly used for CLAS, it is the state of ventral frontal regions that is most critical for slow oscillation generation. Our findings advance models of how CLAS leads to enhancement of slow oscillations, sleep spindles, and associated cognitive benefits, and offer insight into how the effectiveness of brain stimulation techniques can be improved.

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“…Inter-individual variability is a longstanding challenge in fMRI research (Dubois & Adolphs, 2016; Van Horn, Grafton, & Miller, 2008), because group averaging creates a tradeoff between statistical power and relevance to individual participants. Numerous methods exist to tackle inter-individual variability in fMRI data, but the best methods require specific data collection tools or lengthy data collection sessions (Gordon et al, 2017; Michon et al, 2022; Napadow et al, 2008), so analysis-based approaches will remain useful in many situations.…”

Section: Discussionmentioning

confidence: 99%

“…Numerous methods exist to tackle inter-individual variability in fMRI data (Napadow et al, 2008; Gordon et al, 2017; Michon et al, 2022), but not all methods are feasible for all research questions, groups, and protocols. The purpose of COFFEE is to provide a turnkey solution that integrates the established preprocessing methods from Human Connectome Project and Freesurfer, for use with with the popular, accessible fMRI analysis toolbox provided by FSL.…”

Section: Discussionmentioning

confidence: 99%

Reducing individual differences in task fMRI with OGRE (One-step General Registration and Extraction) preprocessing

McAvoy,

Liu,

Zhou

et al. 2023

Preprint

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Numerous methods exist to analyze functional MRI (fMRI) data, but no software currently exists to integrate the commonly-used FSL statistical analysis software with alternative preprocessing methods from the Human Connectome Project. Here we developed the Connectome Operations For FSL ExEcution (COFFEE) pipeline to integrate Human Connectome preprocessing, including Freesurfer brain extraction and registration, for FSL general linear model analysis of volumetric fMRI data. We tested COFFEE on a real fMRI dataset (N=26) wherein participants performed a precision drawing task during fMRI scanning. COFFEE preprocessing, compared to traditional FSL preprocessing, led to lower inter-individual variability across the brain, more precise brain extraction, and greater detected activation in sensorimotor areas contralateral to movement. The COFFEE pipeline provides a turnkey alternative to FSL preprocessing with substantial advantages for analysis of task fMRI data. COFFEE software is available online for research use and further testing.

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Person-specific and precision neuroimaging: Current methods and future directions

Cited by 34 publications

References 135 publications

Longer illness duration is associated with greater individual variability in functional brain activity in Schizophrenia, but not bipolar disorder

Longer illness duration is associated with greater individual variability in functional brain activity in Schizophrenia, but not bipolar disorder

The neurophysiology of closed-loop auditory stimulation in sleep: a magnetoencephalography study

Reducing individual differences in task fMRI with OGRE (One-step General Registration and Extraction) preprocessing

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