This study pioneers the ability of semi-quantitative A/T/N classification using combined 18F-Flutemetamol (A), 18F-MK-6240 (T) and 18F-FDG (N) positron emission tomography (PET) tracers with binary classifications and standardised scales for Centiloid (A), CenTauR (T), and AD metaROI (N), to clinically describe subjects with Mild Cognitive Impairment (MCI) or Mild Dementia (MD) in Alzheimer’s disease (AD). MCI (n = 45, mean age 71.0 ± 8.8, 40

Medical imaging is crucial for glioma management. Combined with MRI, amino acid PET may improve glioma diagnosis, biopsy targeting, and tumor delineation compared to structural MRI alone. Magnetic resonance spectroscopic imaging (MRSI) complements both structural MRI and PET by detecting metabolites such as N-acetylaspartate (NAA), creatine (Cr), and choline (Cho), which are markers for brain health and tumor malignancy, but is challenged by low spatial resolution. This study evaluates whether high-resolution MRSI enhanced by deep learning can improve diagnostic accuracy and serve as a complement or alternative to PET for glioma classification. Ten glioma patients (CNS WHO grades 2-4, ages 24-80) were included. Presurgical [18F]-FACBC PET/MRI, including proton 2D MRSI, was acquired for all patients. Thirty image-guided biopsies were sampled during surgery from the patients and classified as glioma tissue or non-tumor tissue, according to IDH1 status. For each biopsy location, tumor-to-background ratio (TBR) and standardized uptake value (SUV) from PET, and tCho/NAA and tCho/tCr ratios from MRSI were calculated. ROC analysis was used to assess the accuracy of [18F]-FACBC PET and high-resolution MRSI, and the combinations of these in classifying glioma vs non-tumor tissue and IDH1 status. The tCho/NAA ratio from the deep learning-based model demonstrated excellent diagnostic accuracy in classifying glioma vs non-tumor tissue (AUC =0.87, 95% CI: 0.66-1.0), outperforming SUV (AUC =0.71, 95% CI: 0.49-0.90), TBR (AUC =0.68, 95% CI: 0.48-0.86), and tCho/tCr (AUC =0.81, 95% CI: 0.54-1:00). Combining TBR with tCho/NAA and/or tCho/tCr improved tissue classification compared to either modality alone, where TBR+tCho/NAA+tCr/NAA showed the best results (AUC =0.91, 95% CI: 0.71-1.0). MRSI was a poor predictor for IDH1-status (tCho/NAA: AUC =0.67, 95% CI: 0.44-0.88 and tCho/tCr: AUC =0.38, 95% CI: 0.17-0.60), while PET was an excellent predictor (SUV: AUC =0.83, 95% CI: 0.66-0.85 and TBR: AUC =0.82, 95% CI: 0.65-0.94) and the combination of SUV and tCho/tCr was an outstanding predictor (AUC =0.96, 95% CI: 0.88-1.0). Incorporating high-resolution MRSI in combination with [18F]-FACBC PET improved the diagnostic accuracy in differentiating glioma tissue from non-tumor tissue. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement E.B.B. was funded by the Southern Eastern Norway Regional Health Authority (Helse Sor-Ost RHF, HSO; grant number 2021023). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All participants gave written informed consent. This study was performed in accordance with the Declaration of Helsinki and approved by the Regional Ethics Committee (REK South East Norway, reference number: 2018/2243). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Due to ethical and legal restrictions, as well as compliance with the European Union General Data Protection Regulation (GDPR), the dataset acquired and analyzed from St. Olav Hospital and NTNU cannot be made publicly available, as its release would compromise patient privacy. However, investigators may request access to this dataset by contacting Live Eikenes (live.eikenes{at}ntnu.no) at the Faculty of Medicine and Health Sciences, NTNU, Trondheim. Requests will be subject to a data licensing agreement and institutional approval. Further details on the data transfer agreement process can be found here: (https://i.ntnu.no/wiki/-/wiki/Norsk/Dataoverf o ringsavtale/. The code for upscaling metabolite maps is available here: https://github.com/MorEsm/SR-MRSI/

Glioblastoma is characterized by diffuse infiltration, making accurate detection of residual disease essential for improving prognostication and guiding treatment. This study evaluates whether the volume of predicted infiltration, generated by a machine learning (ML) model trained on radiomic features from postoperative magnetic resonance imaging (MRI), is an independent prognostic factor. We analyzed a total of 114 glioblastoma patients, 89 from a retrospective multicenter cohort and 25 from a prospective cohort, who underwent gross total resection and had an early postoperative MRI. A previously published voxel-wise ML model estimated tumor infiltration probability in the non-enhancing peritumoral region using conventional MRI sequences. High-risk of recurrence regions (HRoR) were delineated from the probability maps, and their volumes were quantified. Associations with residual FLAIR volume, clinical variables (age, Karnofsky Performance Status), and survival outcomes (overall survival [OS], progression-free survival [PFS]) were evaluated using Cox regression and Kaplan–Meier analysis. In the retrospective cohort, multivariate Cox modeling confirmed that higher HRoR volume was independently associated with shorter OS (HR = 1.51; 95

The aim of the study was to compare [18F]-FACBC positron emission tomography (PET)-based radiotherapy (RT) volumes to magnetic resonance (MR)-based volumes and investigate the potential impact of including [18F]-FACBC PET in RT treatment planning for gliomas. MR- and PET-defined gross tumor volumes (GTVs) were independently contoured on pre-operative [18F]-FACBC PET/MR images in 24 patients with primary or recurrent low- or high-grade glioma. MR GTVs were defined from regions of contrast-enhancement on T1-weighted (ce-T1) sequences. For non-enhancing tumors or non-enhancing tumor components, regions of FLAIR hyperintensity were also included. PET-based GTVs were delineated using a tumor-to-background ratio (TBR) threshold of 2. GTVs were expanded by an isotropic margin to form clinical target volumes (CTVs). Volumetric analysis was performed using size comparisons, Dice coefficients (DC), overlap coefficients (OC) and Hausdorff distances (HD). PET GTVs were overall significantly smaller than MR GTVs, with median values of 14.8 ccm (6.5–26.7 ccm) and 28.5 ccm (17.2–62.7 ccm), respectively (p = 0.011). No significant volume difference was found between PET and MR volumes when MR volumes were based on ce-T1 only, but PET-based GTVs and CTVs were significantly smaller than MR-based GTVs and CTVs when the latter included FLAIR hyperintensity (p < 0.01). Similarity metrics showed a high degree of concordance between PET and MR volumes in cases where MR volumes were based on ce-T1 only, particularly for CTVs (DC: 0.88 (0.82–0.94), OC: 0.98 (0.96–0.99), HD: 0.86 cm (0.7–1.4 cm)). In comparison, for cases including FLAIR hyperintensity, MR CTVs had a high overlap with PET CTVs (OC: 0.99 (0.96–0.99)), but otherwise significantly lower degree of concordance (DC: 0.66 (0.46–0.81), p < 0.01, HD: 2.65 cm (2.22–3.85 cm), p < 0.001). [18F]-FACBC PET underestimates tumor extension compared to FLAIR in contrast negative tumors and tumors containing non-enhancing components, indicating reduced sensitivity to low-grade tumor tissue and microscopic tumor infiltration. Inclusion of [18F]-FACBC PET as supplement to MR in RT planning for glioma could however help identify regions of highly malignant tumor, to facilitate target volume reduction and dose escalation strategies.

This study is driven by the complex and specialized nature of magnetic resonance spectroscopy imaging (MRSI) data processing, particularly within the scope of brain tumor assessments. Traditional methods often involve intricate manual procedures that demand considerable expertise. In response, we investigate the application of deep neural networks directly to raw MRSI data in the time domain. Given the significant health risks associated with brain tumors, the necessity for early and accurate detection is crucial for effective treatment. While conventional MRI techniques encounter limitations in the rapid and precise spatial evaluation of diffuse gliomas, both accuracy and efficiency are often compromised. MRSI presents a promising alternative by providing detailed insights into tissue chemical composition and metabolic changes. Our proposed model, which utilizes deep neural networks, is specifically designed for the analysis and classification of spectral time series data. Trained on a dataset that includes both synthetic and real MRSI data from brain tumor patients, the model aims to distinguish MRSI voxels that indicate pathological conditions from healthy ones. Our findings demonstrate the model's robustness in classifying glioma-related MRSI voxels from those of healthy tissue, achieving an area under the receiver operating characteristic curve of 0.95. Overall, these results highlight the potential of deep learning approaches to harness raw MR data for clinical applications, signaling a transformative impact on diagnostic and prognostic assessments in brain tumor examinations. Ongoing research is focused on validating these approaches across larger datasets, to establish standardized guidelines and enhance their clinical utility.

Individuals born with very low birth weight (VLBW; < 1500 g) have a higher risk of reduced visual function and brain alterations. In a longitudinal cohort study, we assessed differences in visual outcomes and diffusion metrics from diffusion tensor imaging (DTI) at 3 tesla in the visual white matter pathway and primary visual cortex at age 26 in VLBW adults versus controls and explored whether DTI metrics at 26 years was associated with visual outcomes at 32 years. Thirty-three VLBW adults and 50 term-born controls was included in the study. Visual outcomes included best corrected visual acuity, contrast sensitivity, P100 latency, and retinal nerve fibre layer thickness. Mean diffusivity, axial diffusivity, radial diffusivity, and fractional anisotropy was extracted from seven regions of interest in the visual pathway: splenium, genu, and body of corpus callosum, optic radiations, lateral geniculate nucleus, inferior-fronto occipital fasciculus, and primary visual cortex. On average the VLBW group had lower contrast sensitivity, a thicker retinal nerve fibre layer and higher axial diffusivity and radial diffusivity in genu of corpus callosum and higher radial diffusivity in optic radiations than the control group. Higher fractional anisotropy in corpus callosum areas were associated with better visual function in the VLBW group but not the control group.

This study is motivated by the intricate and expert-demanding nature of magnetic resonance spectroscopy imaging (MRSI) data processing, particularly in the context of brain tumor examinations. Traditional approaches often involve complex manual procedures, requiring substantial expertise. In response, we explore the application of deep neural networks directly on raw MRSI data in the time domain. With brain tumors posing significant health concerns, the imperative for early and accurate detection is paramount for effective treatment. While conventional MRI methods face limitations in rapid and accurate spatial evaluation of diffusive gliomas, accuracy and efficiency are compromised. In contrast, MRSI emerges as a promising tool, offering insights into tissue chemical composition and metabolic alterations. Our proposed model, leveraging deep neural networks, is specifically designed for spectral time series analysis and classification tasks. Trained on a dataset comprising synthetic and real MRSI data from brain tumor patients, the model aims to distinguish MRSI voxels indicative of pathologies from healthy ones. Our results demonstrate the model's robustness in domain transformation, seamlessly adapting from synthetic spectra to in vivo data through a fine-tuning process. Successful classification of MRSI voxels of glioma from healthy tissues underscores the model's potential in clinical applications, signifying a transformative impact on diagnostic and prognostic evaluations in brain tumor examinations. Ongoing research endeavors are directed towards validating these integrated approaches across larger datasets, with the ultimate goal of establishing standardized guidelines and further enhancing their clinical utility.

Background: Gliomas have a heterogeneous nature, and identifying the most aggressive parts of the tumor and defining tumor borders are important for histomolecular diagnosis, surgical resection, and radiation therapy planning. This study evaluated [18F]-FACBC PET for glioma tissue classification. Methods: Pre-surgical [18F]-FACBC PET/MR images were used during surgery and image-localized biopsy sampling in patients with high- and low-grade glioma. TBR was compared to histomolecular results to determine optimal threshold values, sensitivity, specificity, and AUC values for the classification of tumor tissue. Additionally, PET volumes were determined in patients with glioblastoma based on the optimal threshold. [18F]-FACBC PET volumes and diagnostic accuracy were compared to ce-T1 MRI. In total, 48 biopsies from 17 patients were analyzed. Results: [18F]-FACBC had low uptake in non-glioblastoma tumors, but overall higher sensitivity and specificity for the classification of tumor tissue (0.63 and 0.57) than ce-T1 MRI (0.24 and 0.43). Additionally, [18F]-FACBC TBR was an excellent classifier for IDH1-wildtype tumor tissue (AUC: 0.83, 95% CI: 0.71–0.96). In glioblastoma patients, PET tumor volumes were on average eight times larger than ce-T1 MRI volumes and included 87.5% of tumor-positive biopsies compared to 31.5% for ce-T1 MRI. Conclusion: The addition of [18F]-FACBC PET to conventional MRI could improve tumor classification and volume delineation.

Background This PET/MRI study compared contrast-enhanced MRI, 18 F-FACBC-, and 18 F-FDG-PET in the detection of primary central nervous system lymphomas (PCNSL) in patients before and after high-dose methotrexate chemotherapy. Three immunocompetent PCNSL patients with diffuse large B-cell lymphoma received dynamic 18 F-FACBC- and 18 F-FDG-PET/MRI at baseline and response assessment. Lesion detection was defined by clinical evaluation of contrast enhanced T1 MRI (ce-MRI) and visual PET tracer uptake. SUVs and tumor-to-background ratios (TBRs) (for 18 F-FACBC and 18 F-FDG) and time-activity curves (for 18 F-FACBC) were assessed. Results At baseline, seven ce-MRI detected lesions were also detected with 18 F-FACBC with high SUVs and TBRs (SUV max :mean, 4.73, TBR max : mean, 9.32, SUV peak : mean, 3.21, TBR peak :mean: 6.30). High TBR values of 18 F-FACBC detected lesions were attributed to low SUV background . Baseline 18 F-FDG detected six lesions with high SUVs (SUV max : mean, 13.88). In response scans, two lesions were detected with ce-MRI, while only one was detected with 18 F-FACBC. The lesion not detected with 18 F-FACBC was a small atypical MRI detected lesion, which may indicate no residual disease, as this patient was still in complete remission 12 months after initial diagnosis. No lesions were detected with 18 F-FDG in the response scans. Conclusions 18 F-FACBC provided high tumor contrast, outperforming 18 F-FDG in lesion detection at both baseline and in response assessment. 18 F-FACBC may be a useful supplement to ce-MRI in PCNSL detection and response assessment, but further studies are required to validate these findings. Trial registration ClinicalTrials.gov. Registered 15th of June 2017 (Identifier: NCT03188354, https://clinicaltrials.gov/study/NCT03188354 ).

Background:The pursuit of automated methods to assess the extent of resection (EOR) in glioblastomas is challenging, requiring precise measurement of residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was to develop a deep learning-based model for postoperative segmentation using magnetic resonance imaging (MRI). We also compared our model's performance with other available algorithms. Methods:To develop the segmentation model, a training cohort from 3 research institutions and 3 public databases was used. Multiparametric MRI scans with ground truth labels for contrast-enhancing tumor (ET), edema, and surgical cavity, served as training data. The models were trained using MONAI and nnU-Net frameworks. Comparisons were made with currently available segmentation models using an external cohort from a research institution and a public database. Additionally, the model's ability to classify EOR was evaluated using the RANO-Resect classification system. To further validate our best-trained model, an additional independent cohort was used. Results:The study included 586 scans: 395 for model training, 52 for model comparison, and 139 scans for independent validation. The nnU-Net framework produced the best model with median Dice scores of 0.81 for contrast ET, 0.77 for edema, and 0.81 for surgical cavities. Our best-trained model classified patients into maximal and submaximal resection categories with 96% accuracy in the model comparison dataset and 84% in the independent validation cohort. Conclusions:Our nnU-Net-based model outperformed other algorithms in both segmentation and EOR classification tasks, providing a freely accessible tool with promising clinical applicability.

Accurately assessing tumor removal is paramount in the management of glioblastoma. We developed a pipeline using MRI scans and neural networks to segment tumor subregions and the surgical cavity in postoperative images. Our model excels in accurately classifying the extent of resection, offering a valuable tool for clinicians in assessing treatment effectiveness.

The primary aim was to evaluate whether anti-3-[18F]FACBC PET combined with conventional MRI correlated better with histomolecular diagnosis (reference standard) than MRI alone in glioma diagnostics. The ability of anti-3-[18F]FACBC to differentiate between molecular and histopathological entities in gliomas was also evaluated. In this prospective study, patients with suspected primary or recurrent gliomas were recruited from two sites in Norway and examined with PET/MRI prior to surgery. Anti-3-[18F]FACBC uptake (TBRpeak) was compared to histomolecular features in 36 patients. PET results were then added to clinical MRI readings (performed by two neuroradiologists, blinded for histomolecular results and PET data) to assess the predicted tumor characteristics with and without PET. Histomolecular analyses revealed two CNS WHO grade 1, nine grade 2, eight grade 3, and 17 grade 4 gliomas. All tumors were visible on MRI FLAIR. The sensitivity of contrast-enhanced MRI and anti-3-[18F]FACBC PET was 61 https://clinicaltrials.gov/study/NCT04111588

Introduction:The five-class Dixon-based PET/MR attenuation correction (AC) model, which adds bone information to the four-class model by registering major bones from a bone atlas, has been shown to be error-prone. In this study, we introduce a novel method of accounting for bone in pelvic PET/MR AC by directly predicting the errors in the PET image space caused by the lack of bone in four-class Dixon-based attenuation correction.Methods:A convolutional neural network was trained to predict the four-class AC error map relative to CT-based attenuation correction. Dixon MR images and the four-class attenuation correction µ-map were used as input to the models. CT and PET/MR examinations for 22 patients ([18F]FDG) were used for training and validation, and 17 patients were used for testing (6 [18F]PSMA-1007 and 11 [68Ga]Ga-PSMA-11). A quantitative analysis of PSMA uptake using voxel- and lesion-based error metrics was used to assess performance.Results:In the voxel-based analysis, the proposed model reduced the median root mean squared percentage error from 12.1% and 8.6% for the four- and five-class Dixon-based AC methods, respectively, to 6.2%. The median absolute percentage error in the maximum standardized uptake value (SUVmax) in bone lesions improved from 20.0% and 7.0% for four- and five-class Dixon-based AC methods to 3.8%.Conclusion:The proposed method reduces the voxel-based error and SUVmax errors in bone lesions when compared to the four- and five-class Dixon-based AC models.

Despite enormous research interest in diffusion tensor imaging and diffusion kurtosis imaging (DTI; DKI) following mild traumatic brain injury (MTBI), it remains unknown how diffusion in white matter evolves post-injury and relates to acute MTBI characteristics. This prospective cohort study aimed to characterize diffusion changes in white matter the first year after MTBI. Patients with MTBI (n = 193) and matched controls (n = 83) underwent 3T magnetic resonance imaging (MRI) within 72 h and 3- and 12-months post-injury. Diffusion data were analyzed in three steps: 1) voxel-wise comparisons between the MTBI and control group were performed with tract-based spatial statistics at each time-point; 2) clusters of significant voxels identified in step 1 above were evaluated longitudinally with mixed-effect models; 3) the MTBI group was divided into: (A) complicated (with macrostructural findings on MRI) and uncomplicated MTBI; (B) long (1-24 h) and short (< 1 h) post-traumatic amnesia (PTA); and (C) other and no other concurrent injuries to investigate if findings in step 1 were driven mainly by aberrant diffusion in patients with a more severe injury. At 72 h, voxel-wise comparisons revealed significantly lower fractional anisotropy (FA) in one tract and significantly lower mean kurtosis (Kmean) in 11 tracts in the MTBI compared with control group. At 3 months, the MTBI group had significantly higher mean diffusivity in eight tracts compared with controls. At 12 months, FA was significantly lower in four tracts and Kmean in 10 tracts in patients with MTBI compared with controls. There was considerable overlap in affected tracts across time, including the corpus callosum, corona radiata, internal and external capsule, and cerebellar peduncles. Longitudinal analyses revealed that the diffusion metrics remained relatively stable throughout the first year after MTBI. The significant group*time interactions identified were driven by changes in the control rather than the MTBI group. Further, differences identified in step 1 did not result from greater diffusion abnormalities in patients with complicated MTBI, long PTA, or other concurrent injuries, as standardized mean differences in diffusion metrics between the groups were small (0.07 ± 0.11) and non-significant. However, follow-up voxel-wise analyses revealed that other concurrent injuries had effects on diffusion metrics, but predominantly in other metrics and at other time-points than the effects observed in the MTBI versus control group analysis. In conclusion, patients with MTBI differed from controls in white matter integrity already 72 h after injury. Diffusion metrics remained relatively stable throughout the first year after MTBI and were not driven by deviating diffusion in patients with a more severe MTBI.

Abstract The standard surgical goal for operable glioblastomas worldwide is the complete excision of the enhancing tumor. Nevertheless, the peritumoral region harbors infiltrating cells leading to recurrence. Our study aims to evaluate a predictive model designed to predict potential regions of recurrence using voxel-based radiomics analysis on postoperative magnetic resonance imaging (MRI). This retrospective, multi-institutional study involved glioblastoma patients who had undergone complete resection. The training cohort comprised 49 patients from two Spanish institutions, with model evaluation using an external dataset of 27 patients from a Norwegian institution and the public Ivy-Gap dataset. We used follow-up MRI scans for ground truth definition of recurrence, while postoperative multiparametric MRI scans provided the data for extracting voxel-based radiomic features. Our method employed an unsupervised, hybrid approach, which combined a deep neural network and instance optimization, to align the MRI sequences for each patient, registering the follow-up with the postoperative scans. To generate ground truth labels, we segmented the peritumoral region in the postoperative scans, identified as the area showing T2/FLAIR signal alterations, in addition to the enhancing tumor visible in the registered follow-up scan. Overlapping voxels between the peritumoral and enhancing tumor regions were classified as recurrence, while non-overlapping voxels were labeled as nonrecurrence. Radiomic features were subsequently extracted from the postoperative peritumoral region. We trained four machine learning classifiers to predict recurrence. Among these, the Categorical Boosting (CatBoost) classifier demonstrated excellent performance on the test dataset, achieving an average area under the curve (AUC) of 0.83 ± 0.07, and an accuracy of 0.80 ± 0.12, based on voxel-wise comparison with the ground truth. Our study resulted in a method that accurately predicts the area of future tumor recurrence in glioblastoma patients' MRI scans. This innovation could potentially tailor surgical and radiotherapy treatment strategies to these specific areas, possibly extending patient survival.

The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies areas of future recurrence using a voxel-based radiomics analysis of magnetic resonance imaging (MRI) data. This multi-institutional study included a retrospective analysis of patients diagnosed with glioblastoma who underwent surgery with complete resection of the enhancing tumor. Fifty-five patients met the selection criteria. The study sample was split into training (N = 40) and testing (N = 15) datasets. Follow-up MRI was used for ground truth definition, and postoperative structural multiparametric MRI was used to extract voxel-based radiomic features. Deformable coregistration was used to register the MRI sequences for each patient, followed by segmentation of the peritumoral region in the postoperative scan and the enhancing tumor in the follow-up scan. Peritumoral voxels overlapping with enhancing tumor voxels were labeled as recurrence, while non-overlapping voxels were labeled as nonrecurrence. Voxel-based radiomic features were extracted from the peritumoral region. Four machine learning-based classifiers were trained for recurrence prediction. A region-based evaluation approach was used for model evaluation. The Categorical Boosting (CatBoost) classifier obtained the best performance on the testing dataset with an average area under the curve (AUC) of 0.81 ± 0.09 and an accuracy of 0.84 ± 0.06, using region-based evaluation. There was a clear visual correspondence between predicted and actual recurrence regions. We have developed a method that accurately predicts the region of future tumor recurrence in MRI scans of glioblastoma patients. This could enable the adaptation of surgical and radiotherapy treatment to these areas to potentially prolong the survival of these patients.

This study investigated how proactive and reactive cognitive control processing in the brain was associated with habitual sleep health. BOLD fMRI data was acquired from 81 healthy adults with normal sleep (41 females, age 20.96 - 39.58 years) during a test of cognitive control (Not-X CPT). Sleep health was assessed in the week before MRI scanning, using both objective (actigraphy) and self-report measures. Multiple measures indicating poorer sleep health - including later/more variable sleep timing, later chronotype preference, more insomnia symptoms and lower sleep efficiency - were associated with stronger and more widespread BOLD activations in fronto-parietal and subcortical brain regions during cognitive control processing (adjusted for age, sex, education, and fMRI task performance). Most associations were found for reactive cognitive control activation, indicating that poorer sleep health is linked to a ‘hyper-reactive’ brain state. Analysis of time-on-task effects showed that, with longer time on task, poorer sleep health was predominantly associated with increased proactive cognitive control activation, indicating recruitment of additional neural resources over time. Finally, shorter objective sleep duration was associated with lower BOLD activation with time on task and poorer task performance. In conclusion, even in ‘normal sleepers’, relatively poorer sleep health is associated with altered cognitive control processing, possibly reflecting compensatory mechanisms and / or inefficient neural processing.