Fabienne Collette

Post-stroke rehabilitation has traditionally relied on objective assessments of motor and cognitive abilities. However, one crucial factor remains largely overlooked in current practice: the perception of effort, that is, the conscious experience of task intensity and difficulty. Effort perception is strongly influenced by internal states such as fatigue and pain, two symptoms frequently reported after stroke, even in the absence of major neurological deficits. While these symptoms are often treated separately, they converge into a shared experience: that of heightened perceived effort. Here, we propose a model in which fatigue and pain reduce physical and cognitive capacity, requiring greater effort to compensate; this increased effort is in turn perceived and feeds back onto fatigue and pain, creating a self-sustaining loop. Recent literature suggests that both physical and mental fatigue, along with pain, increase perceived effort by impacting available resources and the motivation to engage and persist in cognitive and physical tasks. This modulation can have direct consequences on patient engagement and adherence to rehabilitation. When effort is perceived as disproportionate, even for an objectively simple task, it may lead to reduced participation or even withdrawal from rehabilitation. Conversely, a better understanding of the perception of effort could pave the way for personalized interventions that consider not only patients' actual capacities but also their subjective experiences. It is therefore urgent to consider the perception of effort as a clinical indicator in its own right, one that sits at the intersection of body, brain and lived experience. By integrating this dimension, research could better explain why some patients disengage from post-stroke care despite good recovery potential, and clinical practice could refine its motivational strategies and therapeutic adherence models. The stakes are twofold: to improve understanding of the mechanisms underlying effort perception, and to harness it as a lever for optimizing post-stroke care trajectories.

Post-stroke rehabilitation has traditionally relied on objective assessments of motor and cognitive abilities. However, one crucial factor remains underestimated in current practice: the perception of effort, that is, the conscious experience of task intensity and difficulty. Effort perception is strongly influenced by internal states such as fatigue and pain, two symptoms frequently reported after stroke, even without major neurological deficits. While these symptoms are often treated separately, they converge into a shared experience: heightened perceived effort.Recent literature suggests that both physical and mental fatigue, along with pain, increase perceived effort by impacting available resources and motivation to engage and persist in cognitive and physical tasks. This modulation can directly affect patient engagement and adherence to rehabilitation. When effort is perceived as disproportionate—even for a simple task—it may lead to reduced participation or withdrawal from the care plan. Conversely, a better understanding of effort perception could pave the way for personalized interventions that consider not only patients’ actual capacities but also their subjective experiences.It is therefore urgent to consider the perception of effort as a clinical indicator in its own right—one that sits at the intersection of body, brain and lived experience. By integrating this dimension, research could better explain why some patients disengage from post-stroke care, despite good recovery potential and clinical practice could refine motivational strategies and therapeutic adherence models. The stakes are twofold: improving the understanding of the mechanisms underlying effort perception and harnessing it as a lever for optimizing post-stroke care trajectories.

Quantitative MRI (qMRI) measures relaxation rates, exchange rates, and proton densities that reflect biophysical properties of tissue and are ideally free from protocol- and scanner-dependence. In practice, qMRI has not yet achieved this level of independence from sequence and hardware choice, and quantitative estimates often differ across sites and acquisition schemes. At the same time, pooling data across different sources can be beneficial to statistical power of longitudinal, cross-sectional, or case-control studies. Here, we investigate how protocol and hardware differences can affect pooling data from different sources in large ultra high field (UHF) qMRI studies in the context of healthy aging. We combine the openly available aging UHF qMRI MP2RAGEME-based dataset with two different multiparameter mapping (MPM)-based sets of qMRI data. We evaluate how pooling affects age dependence of qMRI parameters and investigate protocol-related biases, with a particular focus on subcortical structures. We focus the analysis, first, on replication and expansion of the previously published qMRI dataset on normative aging, second, the examination of the protocol influence on the estimated qMRI values, and third, on detecting the protocol effect on the age dependence inferred from the data. We find that the age-related changes in the pooled dataset for R1 measure around 4–17% of the lifespan mean in different structures. Similarly, age-related R2* variation in different structures constitutes around 6–30% of the lifespan mean in the pooled dataset. Subcortical structure volume change is on the order of 5–26%. We further observe larger relative difference between protocols for R1 and volume, while R2* remains more consistent for most regions. We show how pooling the UHF qMRI data from different sites and collected with different quantitative protocols can be both detrimental and beneficial for the analysis outcomes.

Introduction: Cognitive rehabilitation (CR) enhances the autonomy of patients with Alzheimer’s disease. Their daily activities are likely dependent on attention networks. Method: This pilot resting-state fMRI study investigated the cerebral correlates of CR in participants with mild Alzheimer disease (n = 22), compared to a control intervention in patients (n = 21) and in healthy participants (n = 27). Connectivity changes between dorsal and ventral attention networks were expected after 3 months of rehabilitation. Results: A mixed ANOVA comparing pre- and post-intervention data across groups revealed increased connectivity between the dorsal and the ventral attention network following CR (FDR-corrected P = .0072). A post hoc correlation analysis of post-intervention data in the CR group showed that greater autonomy in daily activities was associated with stronger functional relationship between the two attention networks (FDR-corrected P = .0001). Conclusion: Enhanced connectivity between attention networks may be a characteristic of CR benefits in individuals with mild Alzheimer disease.

Mind Blanking (MB) is a mental state characterized by the experience of a seemingly empty mind. This apparent emptiness challenges the theoretical view of a continuous and content-full stream of thought, leading to the question of how it is possible to capture MB events in the first place. MB research has relied largely on probe-caught experience-sampling, during which participants are interrupted at random times to report their mental state. This approach risks missing some MB events as they may occur between probes. Self-caught paradigms, on the other hand, allow participants to report MB upon spontaneously realizing it, relying on meta-awareness. To date, it is not clear whether these two methods capture the same phenomenon neuronally. Here, we investigated whether probe-caught MB (pMB) and self-caught MB (sMB) converge on their behavioral properties and brain correlates. Twenty-two participants underwent 3T fMRI scanning while performing both a pMB and a sMB experience-sampling task. We compared the behavioral properties, BOLD activations and time-varying functional connectivity (FC) across the two approaches. We found that reporting frequencies of pMB and sMB were significantly correlated across individuals, and both MB reports were uniformly distributed over time, pointing towards a behavioral convergence across approaches. However, we observed a divergence in BOLD activations, as sMB recruited significantly greater activation in the dorsal anterior cingulate cortex (dACC) compared to pMB, likely reflecting the meta-awareness and metacognitive monitoring required for self-detecting a MB. Lastly, time-varying FC analysis revealed a convergence, with both pMB and sMB FC resembling a hyperconnectivity pattern compared to other content-full mental states, pointing towards low arousal FC. Together, our results show that sMB and pMB converge on overlapping neurobehavioral correlates. This positions the self-caught method as a complementary tool for studying MB without external interruption, while highlighting the role of meta-awareness in self-report detection.

Dysfunction of the Locus coeruleus (LC), a small brainstem nucleus which consists a primary source of norepinephrine, is linked to various neurological and psychiatric disorders. Light exposure influences many non-image-forming (NIF) biological functions including those modulated by LC activity. Yet direct evidence of light-driven modulation of the LC has remained elusive due to the small size of the structure and deep location. Using ultra-high-field (7 Tesla) functional MRI during an emotional task under varying illuminances, we demonstrate that light modulates LC activity in an emotion-dependent manner. We show that increased illuminance dampens LC responses to negative emotional stimuli while enhancing responses to neutral stimuli. Furthermore, we provide tentative evidence that within an emotional context, light may affect LC activity through the basomedial nucleus of the amygdala as well as through a region of the hypothalamus encompassing the lateral hypothalamus. These findings open avenues for the development of targeted, non-pharmacological treatments leveraging light to modulate norepinephrine tone and/or LC activity in several neuropsychiatric disorders.

Quantitative MRI (qMRI) measures relaxation rates, exchange rates and proton densities that reflect biophysical properties of tissue and are ideally free from protocol- and scanner-dependence. In practice, qMRI has not yet achieved this level of independence from sequence and hardware choice, and quantitative estimates often differ across sites and acquisition schemes. At the same time pooling data across different sources can be beneficial to statistical power of longitudinal, cross-sectional or case-control studies. Here we investigate how protocol and hardware differences can affect pooling data from different sources in large ultra high field (UHF) qMRI studies in the context of healthy aging. We combine the openly available ageing UHF qMRI MP2RAGEME-based dataset with two different MPM-based sets of qMRI data. We evaluate how pooling affects age dependence of qMRI parameters and investigate protocol-related biases, with a particular focus on subcortical structures. We focus the analysis, first, on replication and expansion of the reference qMRI dataset on normative aging, second, the examination of the protocol influence on the estimated qMRI values, and third, on detecting the protocol effect on the age dependence inferred from the data. We find that the age-related changes for R1 measure around 4-17% of the lifespan mean in different structures. Similarly, age-related R2* variation in different structures constitutes around 6-30%. Subcortical structure volume change is on the order of 5-27%. We further observe larger relative difference between protocols for R1 and volume, while R2* remains more consistent for most regions. We show how pooling the UHF qMRI data from different sites and collected with different quantitative protocols can be both detrimental and beneficial for the analysis outcomes.

Background: Light plays a significant role in regulating various non-visual biological processes, such as stimulating alertness and cognition. However, the precise subcortical neural pathways are not fully established, including within the hypothalamus. In particular, how the hypothalamus processes are modulated by time-of-day and developmental stages, remains poorly understood. Methods: In this study, we used 7 Tesla functional magnetic resonance imaging to examine in vivo the impact of different light illuminance (0.16, 37, 92, and 190 melanopic equivalent daylight illuminance: mel-EDI lux) on the activity of the hypothalamus of healthy young adults (N=33; 20 women; 24.3 +/- 3.2y) and adolescents (N=16; 5 women; 16.8 +/- 1.1y) while they completed an auditory executive task, in the morning or in the evening. Results: Performance to the task improved with increasing illuminance irrespective of time of day and age group. When focusing on time-of-day differences in young adults, we found that the regional impact of light illuminance on the activity of the hypothalamus was consistent between the morning and the evening, with the posterior and anterior hypothalamus, respectively, showing increased and decreased activity with increasing illuminance. When focusing on developmental stages differences, during the evening session only, we found similar regional patterns in adolescents and young adult. The magnitude of the response at the highest illuminance was, however, larger in adolescents, with a larger deactivation of the superior-anterior and inferior-tubular hypothalamus. Conclusions: These findings reveal a complex and non-uniform impact of light on hypothalamus activity and provide novel insights into how light influences vary with developmental stages.