Search bioRxivSearch

Biology subjects

Morrow, J.

Publications and source records attributed to Morrow, J..

2 recordsLinked to original sources

Characterizing and minimizing the contribution of sensory inputs to TMS-evoked potentials

BackgroundTranscranial magnetic stimulation (TMS) evokes voltage deflections in electroencephalographic (EEG) recordings, known as TMS-evoked potentials (TEPs), which are increasingly used to study brain dynamics. However, the extent to which TEPs reflect activity directly evoked by magnetic rather than sensory stimulation is unclear.\n\nObjectiveTo characterize and minimize the contribution of sensory inputs to TEPs.\n\nMethodsTwenty-four healthy participants received TMS over the motor cortex using two different intensities (below and above cortical motor threshold) and waveforms (monophasic, biphasic). TMS was also applied over the shoulder as a multisensory control condition. Common sensory attenuation measures, including coil padding and noise masking, were adopted. We examined spatiotemporal relationships between the EEG responses to the scalp and shoulder stimulations at sensor and source levels. Furthermore, we compared three different filters (independent component analysis, signal-space projection with source informed reconstruction (SSP-SIR) and linear regression) designed to attenuate the impact of sensory inputs on TEPs.\n\nResultsThe responses to the scalp and shoulder stimulations were correlated in both temporal and spatial domains, especially after [~]60 ms, regardless of the intensity and stimuli waveform. Among the three filters, SSP-SIR showed the best trade-off between removing sensory-related signals while preserving data not related to the control condition.\n\nConclusionsThe findings demonstrate that TEPs elicited by motor cortex TMS reflect a combination of transcranially and peripherally evoked brain responses despite adopting sensory attenuation methods during experiments, thereby highlighting the importance of adopting sensory control conditions in TMS-EEG studies. Offline filters may help to isolate the transcranial component of the TEP from its peripheral component, but only if these components express different spatiotemporal patterns. More realistic control conditions may help to improve the characterization and attenuation of sensory inputs to TEPs, especially in early responses.

neuroscience

Skeletal muscle MRI differentiates SBMA and ALS and correlates with disease severity

ObjectiveTo investigate the use of muscle MRI for the differential diagnosis and as a disease progression biomarker for two major forms of motor neuron disorders, spinal bulbar muscular atrophy (SBMA) and amyotrophic lateral sclerosis (ALS).\n\nMethodsWe applied quantitative 3-point Dixon and semi-quantitative T1-weighted and STIR imaging to bulbar and lower limb muscles and performed clinical and functional assessments in ALS (n=21) and SBMA (n=21) patients, alongside healthy controls (n=16). Acquired images were analyzed for the presence of fat infiltration or edema as well as specific patterns of muscle involvement. Quantitative MRI measurements were correlated with clinical parameters of disease severity in ALS and SBMA.\n\nResultsQuantitative imaging revealed significant fat infiltration in bulbar (p<0.001) and limb muscles in SBMA compared to controls (thigh: p<0.001; calf: p=0.001), identifying a characteristic pattern of muscle involvement. In ALS, semi-quantitative STIR imaging detected marked hyperintensities in lower limb muscles, distinguishing ALS from SBMA and controls. Lastly, MRI measurements correlated significantly with clinical scales of disease severity in both ALS and SBMA.\n\nConclusionsOur findings show that muscle MRI differentiates between SBMA and ALS and correlates with disease severity, supporting its use as a diagnostic tool and biomarker for disease progression. This highlights the clinical utility of muscle MRI in motor neuron disorders and contributes to establish objective outcome measures, which is crucial for the development of new drugs.

neuroscience