Retrospective comparison of the George Gauge™ registration and the sibilant phoneme registration for constructing OSA oral appliances.

Objective: To compare outcomes for appliances manufactured utilizing the George Gauge™ Registration (GGR) and the Sibilant Phoneme Registration (SPR). It was hypothesized that there would be no difference in outcomes.Methods: This study is a retrospective analysis of two groups. Each group consisted of 20 oral appliances manufactured utilizing the GGR and 20 utilizing the SPR.Results: For the two-site data, no difference in outcomes was demonstrated (p = .24). The SPR method required fewer calibrations (p = 5.6 × 10-3) and less jaw movement (p = 3.33 × 10-4). Both bite methods resulted in similarly variable post-AHI scores (p = .52). For the eight-site data, no difference in outcomes was demonstrated (p = .76). The SPR required less movement of the jaw (p = 4.52 × 10-5); however, outcome variance was larger for the SPR (p = .036).Conclusion: The study null hypothesis of no difference in outcomes was supported.Abbreviations: AHI: Apnea-Hypopnea Index; GGR: George Gauge Registration; SPR: Sibilant Phoneme Registration; OSA: Obstructive Sleep Apnea; OA: Oral Appliance; OAT: Oral Appliance Therapy; MA: Mandibular Advancement; VDO: Vertical Dimensional Opening; AADSM: American Academy of Dental Sleep Medicine; ABDSM: American Board of Dental Sleep Medicine; BMI: Body Mass Index.

From the Group to the Individual in Schizophrenia Spectrum Disorders: Biomarkers of Social Cognitive Impairments and Therapeutic Translation.

People with schizophrenia spectrum disorders (SSDs) often experience persistent social cognitive impairments, associated with poor functional outcome. There are currently no approved treatment options for these debilitating symptoms, highlighting the need for novel therapeutic strategies. Work to date has elucidated differential social processes and underlying neural circuitry affected in SSDs, which may be amenable to modulation using neurostimulation. Further, advances in functional connectivity mapping and electric field modeling may be used to identify individualized treatment targets to maximize the impact of brain stimulation on social cognitive networks. Here, we review literature supporting a roadmap for translating functional connectivity biomarker discovery to individualized treatment development for social cognitive impairments in SSDs. First, we outline the relevance of social cognitive impairments in SSDs. We review machine learning approaches for dimensional brain-behavior biomarker discovery, emphasizing the importance of individual differences. We synthesize research showing that brain stimulation techniques, such as repetitive transcranial magnetic stimulation, can be used to target relevant networks. Further, functional connectivity-based individualized targeting may enhance treatment response. We then outline recent approaches to account for neuroanatomical variability and optimize coil positioning to individually maximize target engagement. Overall, the synthesized literature provides support for the utility and feasibility of this translational approach to precision treatment. The proposed roadmap to translate biomarkers of social cognitive impairments to individualized treatment is currently under evaluation in precision-guided trials. Such a translational approach may also be applicable across conditions and generalizable for the development of individualized neurostimulation targeting other behavioral deficits.

Identifying psychosis spectrum youth using support vector machines and cerebral blood perfusion as measured by arterial spin labeled fMRI.

Altered cerebral blood flow (CBF), as measured by arterial spin labelling (ASL), has been observed in several psychiatric conditions, but is a generally underutilized MRI technique, especially in the study of psychosis spectrum (PS) symptoms. We aimed to determine group differences in ASL resting state functional connectivity (rsFC) between PS and non-PS youth, and the reliability of a support vector machine (SVM) classifier trained on ASL rsFC features to differentiate PS and non-PS youth, especially compared to blood oxygen level dependent (BOLD) fMRI. 1146 youth aged 8-22 with ASL and BOLD data from the Philadelphia Neurodevelopmental Cohort were analyzed. Widespread ASL hyperconnectivity was found in the left cuneus, precuneus, and dorsolateral prefrontal cortex, and hypoconnectivity was found in the left cingulate cortex and orbitofrontal area (multiple linear regression, FDR corrected). An SVM trained on ASL and BOLD features outperformed either modality alone (AUCBOTH = 0.72 versus AUCASL = 0.68 and AUCBOLD = 0.67). Classification performance and precision improved when the non-PS group had no psychiatric comorbidities. The relative success of the classifier suggests ASL rsFC changes exist in PS individuals that differ from BOLD rsFC changes, and extends previous findings of CBF dysregulation in PS.

Inhibit, switch, and update: A within-subject fMRI investigation of executive control.

An influential model of executive control suggests that it comprises three dissociable processes: working memory, inhibition, and task switching. Multiple studies have investigated how these processes are individually implemented in the human brain. However, few have directly investigated this question using a common task architecture and a within-subject design. Here, healthy adult humans (N = 22) performed a novel executive control task during fMRI scanning. The paradigm independently manipulated working memory updating, inhibition, and task switching demands, while keeping all other task features constant. Direct contrasts of each executive task with a closely matched control condition revealed a differentiated pattern of recruitment across control tasks: working memory was associated with activity in dorsolateral prefrontal, lateral parietal and insular cortices bilaterally; Inhibition engaged right lateral and superior medial prefrontal cortex, inferior parietal lobules bilaterally, right middle and inferior temporal cortex, and ventral visual processing regions; Task switching was associated with bilateral activity in medial prefrontal cortex, posterior cingulate cortex and precuneus, as well as left inferior parietal lobule, lateral temporal cortex and right thalamus. A conjunction of all executive versus control task activations revealed common areas of activation overlapping regions of canonical frontoparietal control and dorsal attention networks. Further, multivariate analyses suggest that working memory may be a putative common factor supporting executive functioning. Taken together, these results are consistent with a hybrid model of executive control in the human brain.

Developmentally divergent sexual dimorphism in the cortico-striatal-thalamic-cortical psychosis risk pathway.

Structural and functional cortico-striatal-thalamic-cortical (CSTC) circuit abnormalities have been observed in schizophrenia and the clinical high-risk state. However, this circuit is sexually dimorphic and changes across neurodevelopment. We examined effects of sex and age on structural and functional properties of the CSTC circuit in a large sample of youth with and without psychosis spectrum symptoms (PSS) from the Philadelphia Neurodevelopmental Cohort. T1-weighted and resting-state functional MRI scans were collected on a 3T Siemens scanner, in addition to participants' cognitive and psychopathology data. After quality control, the total sample (aged 11-21) was n = 1095 (males = 485, females = 610). Structural subdivisions of the striatum and thalamus were identified using the MAGeT Brain segmentation tool. Functional seeds were segmented based on brain network connectivity. Interaction effects among PSS group, sex, and age on striatum, thalamus, and subdivision volumes were examined. A similar model was used to test effects on functional connectivity of the CSTC circuit. A sex by PSS group interaction was identified, whereby PSS males had higher volumes and PSS females had lower volumes in striatal and thalamic subdivisions. Reduced functional striato-cortical connectivity was found in PSS youth, primarily driven by males, whereby younger male PSS youth also exhibited thalamo-cortical hypo-connectivity (compared to non-PSS youth), vs. striato-cortical hyper-connectivity in older male PSS youth (compared to non-PSS youth). Youth with PSS demonstrate sex and age-dependent differences in striatal and thalamic subdivision structure and functional connectivity. Further efforts at biomarker discovery and early therapeutic intervention targeting the CSTC circuit in psychosis should consider effects of sex and age.

Ciftify: A framework for surface-based analysis of legacy MR acquisitions.

The preprocessing pipelines of the Human Connectome Project (HCP) were made publicly available for the neuroimaging community to apply the HCP analytic approach to data from non-HCP sources. The HCP analytic approach is surface-based for the cerebral cortex, uses the CIFTI "grayordinate" file format, provides greater statistical sensitivity than traditional volume-based analysis approaches, and allows for a more neuroanatomically-faithful representation of data. However, the HCP pipelines require the acquisition of specific images (namely T2w and field map) that historically have often not been acquired. Massive amounts of this 'legacy' data could benefit from the adoption of HCP-style methods. However, there is currently no published framework, to our knowledge, for adapting HCP preprocessing to "legacy" data. Here we present the ciftify project, a parsimonious analytic framework for adapting key modules from the HCP pipeline into existing structural workflows using FreeSurfer's recon_all structural and existing functional preprocessing workflows. Within this framework, any functional dataset with an accompanying (i.e. T1w) anatomical data can be analyzed in CIFTI format. To simplify usage for new data, the workflow has been bundled with fMRIPrep following the BIDS-app framework. Finally, we present the package and comment on future neuroinformatics advances that may accelerate the movement to a CIFTI-based grayordinate framework.

Separable and Replicable Neural Strategies During Social Brain Function in People With and Without Severe Mental Illness.

OBJECTIVE: Case-control study design and disease heterogeneity may impede biomarker discovery in brain disorders, including serious mental illnesses. To identify biologically and/or behaviorally driven as opposed to diagnostically driven subgroups of individuals, the authors used hierarchical clustering to identify individuals with similar patterns of brain activity during a facial imitate/observe functional MRI task. METHODS: Participants in the Social Processes Initiative in Neurobiology of the Schizophrenia(s) study (N=179; 109 with a schizophrenia spectrum disorder and 70 healthy control participants) underwent MRI scanning at three sites. Hierarchical clustering was used to identify new data-driven groups of participants; differences on social and neurocognitive tests completed outside the scanner were compared among the new groups. RESULTS: Three clusters with distinct patterns of neural activity were found. Cluster membership was not related to diagnosis or scan site. The largest cluster consisted of "typical activators," with activity in the canonical "simulation" circuit. The other clusters represented a "hyperactivating" group and a "deactivating" group. Between-participants Euclidean distances were smaller within clusters than within site or diagnostics groups. The deactivating group had the highest social cognitive and neurocognitive test scores. The hierarchical clustering analysis was repeated on a replication sample (N=108; 32 schizophrenia spectrum disorder, 37 euthymic bipolar disorder, and 39 healthy control participants), which exhibited the same three cluster patterns. CONCLUSIONS: The study findings demonstrate replicable differing patterns of neural activity among individuals during a socio-emotional task, independent of DSM diagnosis or scan site. The findings may provide objective neuroimaging endpoints (biomarkers) for subgroups of individuals in target engagement research aimed at enhancing cognitive performance independent of diagnostic category.

Polygenic Risk and Neural Substrates of Attention-Deficit/Hyperactivity Disorder Symptoms in Youths With a History of Mild Traumatic Brain Injury.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a major sequela of traumatic brain injury (TBI) in youths. The objective of this study was to examine whether ADHD symptoms are differentially associated with genetic risk and brain structure in youths with and without a history of TBI. METHODS: Medical history, ADHD symptoms, genetic data, and neuroimaging data were obtained from a community sample of youths. ADHD symptom severity was compared between those with and without TBI (TBI n = 418, no TBI n = 3193). The relationship of TBI history, genetic vulnerability, brain structure, and ADHD symptoms was examined by assessing 1) ADHD polygenic score (discovery sample ADHD n = 19,099, control sample n = 34,194), 2) basal ganglia volumes, and 3) fractional anisotropy in the corpus callosum and corona radiata. RESULTS: Youths with TBI reported greater ADHD symptom severity compared with those without TBI. Polygenic score was positively associated with ADHD symptoms in youths without TBI but not in youths with TBI. The negative association between the caudate volume and ADHD symptoms was not moderated by a history of TBI. However, the relationship between ADHD symptoms and structure of the genu of the corpus callosum was negative in youths with TBI and positive in youths without TBI. CONCLUSIONS: The identification of distinct ADHD etiology in youths with TBI provides neurobiological insight into the clinical heterogeneity in the disorder. Results indicate that genetic predisposition to ADHD does not increase the risk for ADHD symptoms associated with TBI. ADHD symptoms associated with TBI may be a result of a mechanical insult rather than neurodevelopmental factors.

Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data.

Computational models predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD). Individual prognosis is complicated by many factors including the definition of the prediction objective itself. In this work, we present a computational framework comprising machine-learning techniques for 1) modeling symptom trajectories and 2) prediction of symptom trajectories using multimodal and longitudinal data. We perform primary analyses on three cohorts from Alzheimer's Disease Neuroimaging Initiative (ADNI), and a replication analysis using subjects from Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). We model the prototypical symptom trajectory classes using clinical assessment scores from mini-mental state exam (MMSE) and Alzheimer's Disease Assessment Scale (ADAS-13) at nine timepoints spanned over six years based on a hierarchical clustering approach. Subsequently we predict these trajectory classes for a given subject using magnetic resonance (MR) imaging, genetic, and clinical variables from two timepoints (baseline + follow-up). For prediction, we present a longitudinal Siamese neural-network (LSN) with novel architectural modules for combining multimodal data from two timepoints. The trajectory modeling yields two (stable and decline) and three (stable, slow-decline, fast-decline) trajectory classes for MMSE and ADAS-13 assessments, respectively. For the predictive tasks, LSN offers highly accurate performance with 0.900 accuracy and 0.968 AUC for binary MMSE task and 0.760 accuracy for 3-way ADAS-13 task on ADNI datasets, as well as, 0.724 accuracy and 0.883 AUC for binary MMSE task on replication AIBL dataset.

Spread of activity following TMS is related to intrinsic resting connectivity to the salience network: A concurrent TMS-fMRI study.

Transcranial magnetic stimulation (TMS) modulates activity at local and regions distal to the site of simulation. TMS has also been found to modulate brain networks, and it has been hypothesized that functional connectivity may predict the neuronal changes at local and distal sites in response to a TMS pulse. However, a direct relationship between resting connectivity and change in TMS-induced brain activation has yet to be demonstrated. Concurrent TMS-fMRI is a technique to directly measure this spread activity following TMS in real time. In twenty-two participants, resting-state fMRI scans were acquired, followed by four ten minute sessions of concurrent TMS-fMRI over the left dorsolateral prefrontal cortex (DLPFC). Seed-based functional connectivity to the individualized TMS target was examined using the baseline resting fMRI scan data, and the change of activity resulting from TMS was determined using a general linear model (High vs Low intensity TMS). While at the group level the spatial pattern of resting connectivity related to the pattern of TMS-induced cortical changes, there was substantial variability across individuals. This variability was further probed by examining individual's connectivity from the TMS target to six resting state networks. Only connectivity between the salience network (SN) and the TMS target site correlated with the RSC-TMS score. This suggests that resting state connectivity is correlated with TMS-induced changes in activity following DLPFC stimulation, particularly when the DLPFC target interacts with the SN. These results highlight the importance of examining such relationships at the individual level and may help to guide individual treatment in clinical populations.

A longitudinal human phantom reliability study of multi-center T1-weighted, DTI, and resting state fMRI data.

Multi-center MRI studies can enhance power, generalizability, and discovery for clinical neuroimaging research in brain disorders. Here, we sought to establish the utility of a clustering algorithm as an alternative to more traditional intra-class correlation coefficient approaches in a longitudinal multi-center human phantom study. We completed annual reliability scans on 'travelling human phantoms'. Acquisitions across sites were harmonized prospectively. Twenty-seven MRI sessions were available across four participants, scanned on five scanners, across three years. For each scan, three metrics were extracted: cortical thickness (CT), white matter fractional anisotropy (FA), and resting state functional connectivity (FC). For each metric, hierarchical clustering (Ward's method) was performed. The cluster solutions were compared to participant and scanner using the adjusted Rand index (ARI). For all metrics, data clustered by participant rather than by scanner (ARI > 0.8 comparing clusters to participants, ARI < 0.2 comparing clusters to scanners). These results demonstrate that hierarchical clustering can reliably identify structural and functional scans from different participants imaged on different scanners across time. With increasing interest in data-driven approaches in psychiatric and neurologic brain imaging studies, our findings provide a framework for multi-center analytic approaches aiming to identify subgroups of participants based on brain structure or function.

Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects.

BACKGROUND: Deficits in neurocognition and social cognition are drivers of reduced functioning in schizophrenia spectrum disorders, with potentially shared neurobiological underpinnings. Many studies have sought to identify brain-based biomarkers of these clinical variables using a priori dichotomies (e.g., good vs. poor cognition, deficit vs. nondeficit syndrome). METHODS: We evaluated a fully data-driven approach to do the same by building and validating a brain connectivity-based biomarker of social cognitive and neurocognitive performance in a sample using resting-state and task-based functional magnetic resonance imaging (n = 74 healthy control participants, n = 114 persons with schizophrenia spectrum disorder, 188 total). We used canonical correlation analysis followed by clustering to identify a functional connectivity signature of normal and poor social cognitive and neurocognitive performance. RESULTS: Persons with poor social cognitive and neurocognitive performance were differentiated from those with normal performance by greater resting-state connectivity in the mirror neuron and mentalizing systems. We validated our findings by showing that poor performers also scored lower on functional outcome measures not included in the original analysis and by demonstrating neuroanatomical differences between the normal and poorly performing groups. We used a support vector machine classifier to demonstrate that functional connectivity alone is enough to distinguish normal and poorly performing participants, and we replicated our findings in an independent sample (n = 75). CONCLUSIONS: A brief functional magnetic resonance imaging scan may ultimately be useful in future studies aimed at characterizing long-term illness trajectories and treatments that target specific brain circuitry in those with impaired cognition and function.

Personalized Intrinsic Network Topography Mapping and Functional Connectivity Deficits in Autism Spectrum Disorder.

BACKGROUND: Recent advances in techniques using functional magnetic resonance imaging data demonstrate individually specific variation in brain architecture in healthy individuals. To our knowledge, the effects of individually specific variation in complex brain disorders have not been previously reported. METHODS: We developed a novel approach (Personalized Intrinsic Network Topography, PINT) for localizing individually specific resting-state networks using conventional resting-state functional magnetic resonance imaging scans. Using cross-sectional data from participants with autism spectrum disorder (ASD; n = 393) and typically developing (TD) control participants (n = 496) across 15 sites, we tested: 1) effect of diagnosis and age on the variability of intrinsic network locations and 2) whether prior findings of functional connectivity differences in persons with ASD compared with TD persons remain after PINT application. RESULTS: We found greater variability in the spatial locations of resting-state networks within individuals with ASD compared with those in TD individuals. For TD persons, variability decreased from childhood into adulthood and increased in late life, following a U-shaped pattern that was not present in those with ASD. Comparison of intrinsic connectivity between groups revealed that the application of PINT decreased the number of hypoconnected regions in ASD. CONCLUSIONS: Our results provide a new framework for measuring altered brain functioning in neurodevelopmental disorders that may have implications for tracking developmental course, phenotypic heterogeneity, and ultimately treatment response. We underscore the importance of accounting for individual variation in the study of complex brain disorders.

Integration of routine QA data into mega-analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies.

A novel mega-analytical approach that reduced methodological variance was evaluated using a multisite diffusion tensor imaging (DTI) fractional anisotropy (FA) data by comparing white matter integrity in people with schizophrenia to controls. Methodological variance was reduced through regression of variance captured from quality assurance (QA) and by using Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising. N = 192 (119 patients/73 controls) data sets were collected at three sites equipped with 3T MRI systems: GE MR750, GE HDx, and Siemens Trio. DTI protocol included five b = 0 and 60 diffusion-sensitized gradient directions (b = 1,000 s/mm2 ). In-house DTI QA protocol data was acquired weekly using a uniform phantom; factor analysis was used to distil into two orthogonal QA factors related to: SNR and FA. They were used as site-specific covariates to perform mega-analytic data aggregation. The effect size of patient-control differences was compared to these reported by the enhancing neuro imaging genetics meta-analysis (ENIGMA) consortium before and after regressing QA variance. Impact of MP-PCA filtering was evaluated likewise. QA-factors explained ∼3-4% variance in the whole-brain average FA values per site. Regression of QA factors improved the effect size of schizophrenia on whole brain average FA values-from Cohen's d = .53 to .57-and improved the agreement between the regional pattern of FA differences observed in this study versus ENIGMA from r = .54 to .70. Application of MP-PCA-denoising further improved the agreement to r = .81. Regression of methodological variances captured by routine QA and advanced denoising that led to a better agreement with a large mega-analytic study.

A novel DTI-QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom.

PURPOSE: To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics. METHODS: A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting. RESULTS: The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available. CONCLUSIONS: A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs.

BDNF-Dependent Effects on Amygdala-Cortical Circuitry and Depression Risk in Children and Youth.

The brain-derived neurotrophic factor (BDNF) is critical for brain development, and the functional BDNF Val66Met polymorphism is implicated in risk for mood disorders. The objective of this study was to determine how the Val66Met polymorphism influences amygdala-cortical connectivity during neurodevelopment and assess the relevance for mood disorders. Age- and sex-specific effects of the BDNF Val66Met polymorphism on amygdala-cortical connectivity were assessed by examining covariance of amygdala volumes with thickness throughout the cortex in a sample of Caucasian youths ages 8-22 that were part of the Philadelphia Neurodevelopmental Cohort (n = 339). Follow-up analyses assessed corresponding BDNF genotype effects on resting-state functional connectivity (n = 186) and the association between BDNF genotype and major depressive disorder (MDD) (n = 2749). In adolescents, amygdala-cortical covariance was significantly stronger in Met allele carriers compared with Val/Val homozygotes in amygdala-cortical networks implicated in depression; these differences were driven by females. In follow-up analyses, the Met allele was also associated with stronger resting-state functional connectivity in adolescents and increased likelihood of MDD in adolescent females. The BDNF Val66Met polymorphism may confer risk for mood disorders in females through effects on amygdala-cortical connectivity during adolescence, coinciding with a period in the lifespan when onset of depression often occurs, more commonly in females.

Neural Activity while Imitating Emotional Faces is Related to Both Lower and Higher-Level Social Cognitive Performance.

Imitation and observation of actions and facial emotional expressions activates the human fronto-parietal mirror network. There is skepticism regarding the role of this low-level network in more complex high-level social behaviour. We sought to test whether neural activation during an observation/imitation task was related to both lower and higher level social cognition. We employed an established observe/imitate task of emotional faces during functional MRI in 28 healthy adults, with final analyses based on 20 individuals following extensive quality control. Partial least squares (PLS) identified patterns of relationships between spatial activation and a battery of objective out-of-scanner assessments that index lower and higher-level social cognitive performance, including the Penn emotion recognition task, reading the mind in the eyes, the awareness of social inference test (TASIT) parts 1, 2, and 3, and the relationships across domains (RAD) test. Strikingly, activity in limbic, right inferior frontal, and inferior parietal areas during imitation of emotional faces correlated with performance on emotion evaluation (TASIT1), social inference - minimal (TASIT2), social inference - enriched (TASIT3), and the RAD tests. These results show a role for this network in both lower-level and higher-level social cognitive processes which are collectively critical for social functioning in everyday life.

Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest.

Anticorrelation between the default and dorsal attention networks is a central feature of human functional brain organization. Hallmarks of aging include impaired default network modulation and declining medial temporal lobe (MTL) function. However, it remains unclear if this anticorrelation is preserved into older adulthood during task performance, or how this is related to the intrinsic architecture of the brain. We hypothesized that older adults would show reduced within- and increased between-network functional connectivity (FC) across the default and dorsal attention networks. To test this hypothesis, we examined the effects of aging on task-related and intrinsic FC using functional magnetic resonance imaging during an autobiographical planning task known to engage the default network and during rest, respectively, with young (n = 72) and older (n = 79) participants. The task-related FC analysis revealed reduced anticorrelation with aging. At rest, there was a robust double dissociation, with older adults showing a pattern of reduced within-network FC, but increased between-network FC, across both networks, relative to young adults. Moreover, older adults showed reduced intrinsic resting-state FC of the MTL with both networks suggesting a fractionation of the MTL memory system in healthy aging. These findings demonstrate age-related dedifferentiation among these competitive large-scale networks during both task and rest, consistent with the idea that age-related changes are associated with a breakdown in the intrinsic functional architecture within and among large-scale brain networks.