Beta and gamma oscillations are the dominant oscillatory activity in the human motor cortex (M1). However, their physiological basis and precise functional significance remain poorly understood. Here, we used transcranial magnetic stimulation (TMS) to examine the physiological basis and behavioral relevance of driving beta and gamma oscillatory activity in the human M1 using transcranial alternating current stimulation (tACS). tACS was applied using a sham-controlled crossover design at individualized intensity for 20 min and TMS was performed at rest (before, during, and after tACS) and during movement preparation (before and after tACS). We demonstrated that driving gamma frequency oscillations using tACS led to a significant, duration-dependent decrease in local resting-state GABA_Ainhibition, as quantified by short interval intracortical inhibition. The magnitude of this effect was positively correlated with the magnitude of GABA_Adecrease during movement preparation, when gamma activity in motor circuitry is known to increase. In addition, gamma tACS-induced change in GABA_Ainhibition was closely related to performance in a motor learning task such that subjects who demonstrated a greater increase in GABA_Ainhibition also showed faster short-term learning. The findings presented here contribute to our understanding of the neurophysiological basis of motor rhythms and suggest that tACS may have similar physiological effects to endogenously driven local oscillatory activity. Moreover, the ability to modulate local interneuronal circuits by tACS in a behaviorally relevant manner provides a basis for tACS as a putative therapeutic intervention.SIGNIFICANCE STATEMENTGamma oscillations have a vital role in motor control. Using a combined tACS-TMS approach, we demonstrate that driving gamma frequency oscillations modulates GABA_Ainhibition in the human motor cortex. Moreover, there is a clear relationship between the change in magnitude of GABA_Ainhibition induced by tACS and the magnitude of GABA_Ainhibition observed during task-related synchronization of oscillations in inhibitory interneuronal circuits, supporting the hypothesis that tACS engages endogenous oscillatory circuits. We also show that an individual's physiological response to tACS is closely related to their ability to learn a motor task. These findings contribute to our understanding of the neurophysiological basis of motor rhythms and their behavioral relevance and offer the possibility of developing tACS as a therapeutic tool.

Electrophysiological recordings of neuronal activity show spontaneous and task-dependent changes in their frequency-domain power spectra. These changes are conventionally interpreted as modulations in the amplitude of underlying oscillations. However, this overlooks the possibility of underlying transient spectral ‘bursts’ or events whose dynamics can map to changes in trial-average spectral power in numerous ways. Under this emerging perspective, a key challenge is to perform burst detection, i.e. to characterise single-trial transient spectral events, in a principled manner. Here, we describe how transient spectral events can be operationalised and estimated using Hidden Markov Models (HMMs). The HMM overcomes a number of the limitations of the standard amplitude-thresholding approach to burst detection; in that it is able to concurrently detect different types of bursts, each with distinct spectral content, without the need to predefine frequency bands of interest, and does so with less dependence on a priori threshold specification. We describe how the HMM can be used for burst detection and illustrate its benefits on simulated data. Finally, we apply this method to empirical data to detect multiple burst types in a task-MEG dataset, and illustrate how we can compute burst metrics, such as the task-evoked timecourse of burst duration.

Background: The global rise in kidney diseases underscores the need for reliable, noninvasive imaging biomarkers. Among these, renal cortical T1 has shown promise but further technical validation is still required.Purpose: To evaluate the repeatability, reproducibility, and observer variability of kidney cortical T1 mapping in human volunteers without known renal disease.Study Type: Prospective.Subjects: Three cohorts without renal disease: 1) 25 volunteers (median age 38 [interquartile range, IQR: 28–42] years, female N = 11) for scan–rescan assessments on GE 1.5 T and Siemens 1.5 T; 2) 29 volunteers (median age 29 [IQR: 24–40] years, female N = 15) for scan–rescan assessments on Siemens 3 T; and 3) 16 volunteers (median age 34 [IQR: 31–42] years, female N = 8) for cross‐scanner reproducibility.Field Strength/Sequences: 1.5 T and 3 T, a modified Look‐Locker imaging (MOLLI) sequence with a balanced steady‐state free precession (bSSFP) readout.Assessment: Kidney cortical T1 data was acquired on GE 1.5 T scanner, Siemens 1.5 T and 3 T scanners. Within‐scanner repeatability and inter/intra‐observer variability: GE 1.5 T and Siemens 1.5 T, and cross‐scanner manufacturer reproducibility: Siemens 1.5 T–GE 1.5 T.Statistical Tests: Bland Altman analysis, coefficient of variation (CoV), intra‐class coefficient (ICC), and repeatability coefficient (RC).Results: Renal cortical T1 mapping showed high repeatability and reliability across scanner field strengths and manufacturers (repeatability: CoV 1.9%–2.8%, ICC 0.79–0.88, pooled RC 73 msec; reproducibility: CoV 3.0%, ICC 0.75, RC 90 msec). The method also showed robust observer variability (CoV 0.6%–1.4%, ICC 0.93–0.98, RC 22–48 msec).Data Conclusion: Kidney cortical T1 mapping is a highly repeatable and reproducible method across MRI manufacturers, field strengths, and observer conditions.Evidence Level: 2Technical Efficacy: Stage 2

The escalating global incidence of obesity, cardiometabolic disease and sarcopenia necessitates reliable body composition measurement tools. MRI-based assessment is the gold standard, with utility in both clinical and drug trial settings. This study aims to validate a new automated volumetric MRI method by comparing with manual ground truth, prior volumetric measurements, and against a new method for semi-automated single-slice area measurements. 4905 individuals from the UK Biobank with repeat whole-body Dixon MRI scans were selected. MRI data were processed automatically to derive new (1) volumetric and (2) single-slice area measurements at L3 vertebral level for visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and abdominal skeletal muscle (SM). For comparison, prior volumetric measurements of VAT and SAT were included. A separate set of scans from 100 subjects was randomly selected and body composition volumes and areas were manually segmented as ground truth. The new automated volumetric measurements were found to have excellent agreement with manual ground truth with no substantial bias (ICC ≥ 0.96, CoV ≤ 3.8%). In the cohort of 4905 individuals (49% male, mean age 62 years ± 8, BMI 26 kg/m² ± 4), we confirmed that prior and new volumetric methods of VAT and SAT measurement were very strongly correlated (VAT: ρ = 0.99; SAT: ρ = 1; both p < 0.001). Single-slice L3 area measurements demonstrated very strong correlations with corresponding volumes (for VAT ρ = 0.97, for SAT ρ = 0.94, for SM ρ = 0.95, all p < 0.001), and remained excellent across sex, age, and cardiometabolic characteristics (median ρ = 0.95). We found robust correlations between manually segmented, automated volumetric, and semi-automated single-slice body composition methods. The interchangeability of these methods suggests that for each application the method should be selected according to practical considerations, operational differences and measurement granularity, rather than technical performance.

Efficient enrolment remains a major hurdle for MASH/MASLD trials, many of which require liver fat content (LFC) ≥ 8%. We aimed to develop and validate a predictive score based on routine clinical data, optimised for patients with type 2 diabetes (T2D) to prescreen individuals likely to have elevated LFC and thereby reduce screen failures and facilitate clinical trial enrolment. Data were pooled from multiple clinical studies ( N = 1075, 53% male, age: 54 years) spanning a broad BMI and including individuals with suspected or confirmed MASLD ( N = 694) and those with T2D ( N = 478). LFC was quantified by MRI proton‐density fat fraction (MRI‐PDFF) or proton magnetic resonance spectroscopy ( ¹ H‐MRS). The study cohort was dichotomised at < 8% vs. ≥ 8% LFC, a criterion for clinical trial eligibility. Candidate models were trained and tested using multivariable logistic regression; comparators included the fatty liver index (FLI), hepatic steatosis index (HSI), and, where available, Vibration‐Controlled Transient Elastography with Controlled Attenuation Parameter (VCTE‐CAP). The ALERT score, based on ALT, triglycerides and T2D status, showed high accuracy for detecting LFC ≥ 8% (AUC 0.86, 95% CI 0.81–0.91), outperforming FLI (AUC 0.80, CI 0.74–0.86), HSI (AUC 0.76, CI 0.70–0.83), and VCTE‐CAP (AUC 0.66, CI 0.58–0.74). Using a threshold of 0.60, ALERT achieved 91% specificity and 85% PPV. The overall screen failure rate was 5%, indicating strong enrichment for LFC ≥ 8%. The ALERT score, a simple three‐variable score, enriches for LFC ≥ 8% with high specificity and PPV, offering a practical tool to streamline MASH/MASLD Phase 2–3 trial enrolment. The tool's performance in T2D and in a diverse real‐world population supports its broad generalisability to current trial settings.