Optogenetic fMRI reveals therapeutic circuits of subthalamic nucleus deep brain stimulation.

While deep brain stimulation (DBS) is widely employed for managing motor symptoms in Parkinson's disease (PD), its exact circuit mechanisms remain controversial. To identify the neural targets affected by therapeutic DBS in PD, we analyzed DBS-evoked whole brain activity in female hemi-parkinsonian rats using function magnetic resonance imaging (fMRI). We delivered subthalamic nucleus (STN) DBS at various stimulation pulse repetition rates using optogenetics, allowing unbiased examinations of cell-type specific STN feed-forward neural activity. Unilateral STN optogenetic stimulation elicited pulse repetition rate-dependent alterations of blood-oxygenation-level-dependent (BOLD) signals in SNr (substantia nigra pars reticulata), GP (globus pallidus), and CPu (caudate putamen). Notably, these manipulations effectively ameliorated pathological circling behavior in animals expressing the kinetically faster Chronos opsin, but not in animals expressing ChR2. Furthermore, mediation analysis revealed that the pulse repetition rate-dependent behavioral rescue was significantly mediated by optogenetically induced activity changes in GP and CPu, but not in SNr. This suggests that the activation of GP and CPu are critically involved in the therapeutic mechanisms of STN DBS.

Intrinsic Functional Connectivity between the Anterior Insular and Retrosplenial Cortex as a Moderator and Consequence of Cocaine Self-Administration in Rats.

While functional brain imaging studies in humans suggest that chronic cocaine use alters functional connectivity (FC) within and between key large-scale brain networks, including the default mode network (DMN), the salience network (SN), and the central executive network (CEN), cross-sectional studies in humans are challenging to obtain brain FC prior to cocaine use. Such information is critical to reveal the relationship between individual's brain FC and the subsequent development of cocaine dependence and brain changes during abstinence. Here, we performed a longitudinal study examining functional magnetic resonance imaging (fMRI) data in male rats (n = 7), acquired before cocaine self-administration (baseline), on 1 d of abstinence following 10 d of cocaine self-administration, and again after 30 d of experimenter-imposed abstinence. Using repeated-measures analysis of variance (ANOVA) with network-based statistics (NBS), significant connectivity changes were found between anterior insular cortex (AI) of the SN, retrosplenial cortex (RSC) of the DMN, somatosensory cortex, and caudate-putamen (CPu), with AI-RSC FC showing the most robust changes between baseline and 1 d of abstinence. Additionally, the level of escalated cocaine intake is associated with AI-RSC and AI-CPu FC changes between 1 d and 30 d of abstinence; further, the subjects' AI-RSC FC prior to cocaine intake is a significant moderator for the AI-RSC changes during abstinence. These results provide novel insights into the roles of AI-RSC FC before and after cocaine intake and suggest this circuit to be a potential target to modulate large-scale network and associated behavioral changes in cocaine use disorders.

Acute alcohol induces greater dose-dependent increase in the lateral cortical network functional connectivity in adult than adolescent rats.

Alcohol misuse and, particularly adolescent drinking, is a major public health concern. While evidence suggests that adolescent alcohol use affects frontal brain regions that are important for cognitive control over behavior little is known about how acute alcohol exposure alters large-scale brain networks and how sex and age may moderate such effects. Here, we employ a recently developed functional magnetic resonance imaging (fMRI) protocol to acquire rat brain functional connectivity data and use an established analytical pipeline to examine the effect of sex, age, and alcohol dose on connectivity within and between three major rodent brain networks: defaul mode, salience, and lateral cortical network. We identify the intra- and inter-network connectivity differences and establish moderation models to reveal significant influences of age on acute alcohol-induced lateral cortical network connectivity. Through this work, we make brain-wide isotropic fMRI data with acute alcohol challenge publicly available, with the hope to facilitate future discovery of brain regions/circuits that are causally relevant to the impact of acute alcohol use.

Accumulation of network redundancy marks the early stage of Alzheimer's disease.

Brain wiring redundancy counteracts aging-related cognitive decline by reserving additional communication channels as a neuroprotective mechanism. Such a mechanism plays a potentially important role in maintaining cognitive function during the early stages of neurodegenerative disorders such as Alzheimer's disease (AD). AD is characterized by severe cognitive decline and involves a long prodromal stage of mild cognitive impairment (MCI). Since MCI subjects are at high risk of converting to AD, identifying MCI individuals is essential for early intervention. To delineate the redundancy profile during AD progression and enable better MCI diagnosis, we define a metric that reflects redundant disjoint connections between brain regions and extract redundancy features in three high-order brain networks-medial frontal, frontoparietal, and default mode networks-based on dynamic functional connectivity (dFC) captured by resting-state functional magnetic resonance imaging (rs-fMRI). We show that redundancy increases significantly from normal control (NC) to MCI individuals and decreases slightly from MCI to AD individuals. We further demonstrate that statistical features of redundancy are highly discriminative and yield state-of-the-art accuracy of up to 96.8 ± 1.0% in support vector machine (SVM) classification between NC and MCI individuals. This study provides evidence supporting the notion that redundancy serves as a crucial neuroprotective mechanism in MCI.

Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli.

The default mode network (DMN) is critical for self-referential mental processes, and its dysfunction is implicated in many neuropsychiatric disorders. However, the neurophysiological properties and task-based functional organization of the rodent DMN are poorly understood, limiting its translational utility. Here, we combine fiber photometry with functional magnetic resonance imaging (fMRI) and computational modeling to characterize dynamics of putative rat DMN nodes and their interactions with the anterior insular cortex (AI) of the salience network. Our analysis revealed neuronal activity changes in AI and DMN nodes preceding fMRI-derived DMN activations and cyclical transitions between brain network states. Furthermore, we demonstrate that salient oddball stimuli suppress the DMN and enhance AI neuronal activity and that the AI causally inhibits the retrosplenial cortex, a prominent DMN node. These findings elucidate the neurophysiological foundations of the rodent DMN, its spatiotemporal dynamical properties, and modulation by salient stimuli, paving the way for future translational studies.

Human thirst behavior requires transformation of sensory inputs by intrinsic brain networks.

BACKGROUND: To survive and thrive, many animals, including humans, have evolved goal-directed behaviors that can respond to specific physiological needs. An example is thirst, where the physiological need to maintain water balance drives the behavioral basic instinct to drink. Determining the neural basis of such behaviors, including thirst response, can provide insights into the way brain-wide systems transform sensory inputs into behavioral outputs. However, the neural basis underlying this spontaneous behavior remains unclear. Here, we provide a model of the neural basis of human thirst behavior. RESULTS: We used fMRI, coupled with functional connectivity analysis and serial-multiple mediation analysis, we found that the physiological need for water is first detected by the median preoptic nucleus (MnPO), which then regulates the intention of drinking via serial large-scale spontaneous thought-related intrinsic network interactions that include the default mode network, salience network, and frontal-parietal control network. CONCLUSIONS: Our study demonstrates that the transformation in humans of sensory inputs for a single physiological need, such as to maintain water balance, requires large-scale intrinsic brain networks to transform this input into a spontaneous human behavioral response.

Divergent and Convergent Imaging Markers Between Bipolar and Unipolar Depression Based on Machine Learning.

Distinguishing bipolar depression (BD) from unipolar depression (UD) based on symptoms only is challenging. Brain functional connectivity (FC), especially dynamic FC, has emerged as a promising approach to identify possible imaging markers for differentiating BD from UD. However, most of such studies utilized conventional FC and group-level statistical comparisons, which may not be sensitive enough to quantify subtle changes in the FC dynamics between BD and UD. In this paper, we present a more effective individualized differentiation model based on machine learning and the whole-brain "high-order functional connectivity (HOFC)" network. The HOFC, capturing temporal synchronization among the dynamic FC time series, a more complex "chronnectome" metric compared to the conventional FC, was used to classify 52 BD, 73 UD, and 76 healthycontrols (HC). We achieved a satisfactory accuracy (70.40%) in BD vs. UD differentiation. The resultant contributing features revealed the involvement of the coordinated flexible interactions among sensory (e.g., olfaction, vision, and audition), motor, and cognitive systems. Despite sharing common chronnectome of cognitive and affective impairments, BD and UD also demonstrated unique dynamic FC synchronization patterns. UD is more associated with abnormal visual-somatomotor inter-network connections, while BD is more related to impaired ventral attention-frontoparietal inter-network connections. Moreover, we found that the illness duration modulated the BD vs. UD separation, with the differentiation performance hampered by the secondary disease effects. Our findings suggest that BD and UD may have divergent and convergent neural substrates, which further expand our knowledge of the two different mental disorders.

Chemogenetic stimulation of tonic locus coeruleus activity strengthens the default mode network.

The default mode network (DMN) of the brain is functionally associated with a wide range of behaviors. In this study, we used functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and spectral fiber photometry to investigate the selective neuromodulatory effect of norepinephrine (NE)-releasing noradrenergic neurons in the locus coeruleus (LC) on the mouse DMN. Chemogenetic-induced tonic LC activity decreased cerebral blood volume (CBV) and glucose uptake and increased synchronous low-frequency fMRI activity within the frontal cortices of the DMN. Fiber photometry results corroborated these findings, showing that LC-NE activation induced NE release, enhanced calcium-weighted neuronal spiking, and reduced CBV in the anterior cingulate cortex. These data suggest that LC-NE alters conventional coupling between neuronal activity and CBV in the frontal DMN. We also demonstrated that chemogenetic activation of LC-NE neurons strengthened functional connectivity within the frontal DMN, and this effect was causally mediated by reduced modulatory inputs from retrosplenial and hippocampal regions to the association cortices of the DMN.

Computing hemodynamic response functions from concurrent spectral fiber-photometry and fMRI data.

Significance: Although emerging evidence suggests that the hemodynamic response function (HRF) can vary by brain region and species, a single, canonical, human-based HRF is widely used in animal studies. Therefore, the development of flexible, accessible, brain-region specific HRF calculation approaches is paramount as hemodynamic animal studies become increasingly popular. Aim: To establish an fMRI-compatible, spectral, fiber-photometry platform for HRF calculation and validation in any rat brain region. Approach: We used our platform to simultaneously measure (a) neuronal activity via genetically encoded calcium indicators (GCaMP6f), (b) local cerebral blood volume (CBV) from intravenous Rhodamine B dye, and (c) whole brain CBV via fMRI with the Feraheme contrast agent. Empirical HRFs were calculated with GCaMP6f and Rhodamine B recordings from rat brain regions during resting-state and task-based paradigms. Results: We calculated empirical HRFs for the rat primary somatosensory, anterior cingulate, prelimbic, retrosplenial, and anterior insular cortical areas. Each HRF was faster and narrower than the canonical HRF and no significant difference was observed between these cortical regions. When used in general linear model analyses of corresponding fMRI data, the empirical HRFs showed better detection performance than the canonical HRF. Conclusions: Our findings demonstrate the viability and utility of fiber-photometry-based HRF calculations. This platform is readily scalable to multiple simultaneous recording sites, and adaptable to study transfer functions between stimulation events, neuronal activity, neurotransmitter release, and hemodynamic responses.

Altered Connectedness of the Brain Chronnectome During the Progression to Alzheimer's Disease.

Graph theory has been extensively used to investigate brain network topology and its changes in disease cohorts. However, many graph theoretic analysis-based brain network studies focused on the shortest paths or, more generally, cost-efficiency. In this work, we use two new concepts, connectedness and 2-connectedness, to measure different global properties compared to the previously widely adopted ones. We apply them to unravel interesting characteristics in the brain, such as redundancy design and further conduct a time-varying brain functional network analysis for characterizing the progression of Alzheimer's disease (AD). Specifically, we define different connectedness and 2-connectedness states and evaluate their dynamics in AD and its preclinical stage, mild cognitive impairment (MCI), compared to the normal controls (NC). Results indicate that, compared to MCI and NC, brain networks of AD tend to be more frequently connected at a sparse level. For MCI, we found that their brains are more likely to be 2-connected in the minimal connected state as well indicating increasing redundancy in brain connectivity. Such a redundant design could ensure maintained connectedness of the MCI's brain network in the case that pathological damages break down any link or silenced any node, making it possible to preserve cognitive abilities. Our study suggests that the redundancy in the brain functional chronnectome could be altered in the preclinical stage of AD. The findings can be successfully replicated in a retest study and with an independent MCI dataset. Characterizing redundancy design in the brain chronnectome using connectedness and 2-connectedness analysis provides a unique viewpoint for understanding disease affected brain networks.

Association between Dietary Patterns and Serum Hepatic Enzyme Levels in Adults with Dyslipidemia and Impaired Fasting Plasma Glucose.

We investigated the association between dietary patterns and serum hepatic enzyme levels in adults with dyslipidemia and impaired fasting glucose in Taiwan. A total of 15,005 subjects (5452 men and 9553 women) aged 35-69 years were selected. Two major dietary patterns were identified by principal component analysis: Western dietary pattern and Mediterranean dietary pattern. Subjects in the highest quartile (Q4) of the Western dietary pattern showed an increased risk of elevated serum alanine aminotransferase (ALT) levels (OR: 1.24, 95% CI: 1.06-1.45, p-trend = 0.01). Fur-thermore, in the highest quartile of the Western dietary pattern, subjects with high waist circum-ference were observed to have a greater risk for developing abnormal serum ALT levels compared to those in the lowest quartile (Q1) (OR: 1.43, 95% CI: 1.04-1.97, p-trend = 0.01). In the highest quartile of the Western dietary pattern, only women were at an increased risk for having abnormal serum ALT levels (OR: 1.28, 95% CI: 1.04-1.59, p-trend = 0.03). By contrast, in the highest quartile of the Mediterranean dietary pattern, only men were at a reduced risk for having abnormal serum gamma-glutamyl transferase (GGT) levels (OR: 0.72, 95% CI: 0.53-0.97, p-trend = 0.048). We report a positive association between the Western dietary pattern and abnormal serum ALT levels.

3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data.

Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation, Pulse-Coupled Neural Network, SHape descriptor selected External Regions after Morphologically filtering, and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, center-of-mass distance, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community. Significant Methodological Contribution: We proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.

Spontaneous thought-related network connectivity predicts sertraline effect on major depressive disorder.

Sertraline is one of the most commonly prescribed antidepressants. Major depressive disorder (MDD) is characterized by spontaneous thoughts that are laden with negative affect-a "malignant sadness". Prior neuroimaging studies have identified abnormal resting-state functional connectivity (rsFC) in the spontaneous brain networks of MDD patients. But how antidepressant medication acts to relieve the experience of depression as well as adjust its associated spontaneous networks and mood-regulation circuits remains an open question. In this study, we recruited 22 drug-naïve MDD patients along with 35 normal controls and investigated whether the functional integrity of cortical networks associated with spontaneous thoughts is modulated by sertraline treatment. We attempted to predict post-treatment effects based upon what we observed in the pre-treatment rsFC of drug-naïve MDD patients. In the result, we demonstrated that (1) after the sertraline treatment, the medial temporal lobe of default network (DNMTL) and mood regulation pathway-the fronto-parietal control network (FPCN), the thalamus, and the salience network (SN)-were restored to normal connectivity, relative to the pre-treatment condition; however, the altered connections of FPCN-core DN (DNCORE), FPCN-SN, and intra-FPCN among MDD patients remained impaired; (2) thalamo-prefrontal connectivity provides moderate predictive power (r2 = 0.63) for the effectiveness of sertraline treatment. In summary, our findings contribute to a body of evidence that suggests salubrious effects of sertraline treatment primarily involve the FPCN-thalamus-SN pathway. The pre-treatment rsFC in this pathway could serve as a predictor of sertraline treatment outcome.

Think Hard or Think Smart: Network Reconfigurations After Divergent Thinking Associate With Creativity Performance.

Evidence suggests divergent thinking is the cognitive basis of creative thoughts. Neuroimaging literature using resting-state functional connectivity (RSFC) has revealed network reorganizations during divergent thinking. Recent studies have revealed the changes of network organizations when performing creativity tasks, but such brain reconfigurations may be prolonged after task and be modulated by the trait of creativity. To investigate the dynamic reconfiguration, 40 young participants were recruited to perform consecutive Alternative Uses Tasks (AUTs) for divergent thinking and two resting-state scans (before and after AUT) were used for mapping the brain reorganizations after AUT. We split participants into high- and low-creative groups based on creative achievement questionnaire (CAQ) and targeted on reconfigurations of the two brain networks: (1) default-mode network (DMN) and (2) the network seeded at the left inferior frontal gyrus (IFG) because the between-group difference of AUT-induced brain activation located at the left IFG. The changes of post-AUT RSFCs (DMN and IFGN) indicated the prolonged effect of divergent thinking. More specifically, the alterations of RSFCIFG-AG and RSFCIFG-IPL (AG: angular gyrus, IPG: inferior parietal lobule) in the high-creative group had positive relationship with their AUT performances (originality and fluency), but not found in the low-creative group. Furthermore, the RSFC changes of DMN did not present significant relationships with AUT performances. The findings not only confirmed the possibility of brain dynamic reconfiguration following divergent thinking, but also suggested the distinct IFGN reconfiguration between individuals with different creativity levels.

Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net.

Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans, because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved superior averaged Dice similarity coefficient to ground truth T2-weighted rapid acquisition with relaxation enhancement and T2∗-weighted echo planar imaging data in both rats and mice (all p < 0.05), demonstrating robust performance of our approach across various MRI protocols.

Synchrony Between Default-Mode and Sensorimotor Networks Facilitates Motor Function in Stroke Rehabilitation: A Pilot fMRI Study.

Stroke is the most common cause of complex disability in Taiwan. After stroke onset, persistent physical practice or exercise in the rehabilitation procedure reorganizes neural assembly for reducing motor deficits, known as neuroplasticity. Neuroimaging literature showed rehabilitative effects specific to the brain networks of the sensorimotor network (SMN) and default-mode network (DMN). However, whether between-network interactions facilitate the neuroplasticity after stroke rehabilitation remains a mystery. Therefore, we conducted the longitudinal assessment protocol of stroke rehabilitation, including three types of clinical evaluations and two types of functional magnetic resonance imaging (fMRI) techniques (resting state and grasp task). Twelve chronic stroke patients completed the rehabilitation protocol for at least 24 h and finished the three-time assessments: before, after rehabilitation, and 1 month after the cessation of rehabilitation. For comparison, age-matched normal controls (NC) underwent the same fMRI evaluation once without repeated measure. Increasing scores of the Fugl-Meyer assessment (FMA) and upper extremity performance test reflected the enhanced motor performances after the stroke rehabilitation process. Analysis of covariance (ANCOVA) results showed that the connections between posterior cingulate cortex (PCC) and iM1 were persistently enhanced in contrast to the pre-rehabilitation condition. The interactions between PCC and SMN were positively associated with motor performances. The enhanced cross-network connectivity facilitates the motor recovery after stroke rehabilitation, but the cross-network interaction was low before the rehabilitation process, similar to the level of NCs. Our findings suggested that cross-network connectivity plays a facilitatory role following the stroke rehabilitation, which can serve as a neurorehabilitative biomarker for future intervention evaluations.

Functional Connectivity of Hippocampal CA3 Predicts Neurocognitive Aging via CA1-Frontal Circuit.

The CA3 and CA1 principal cell fields of the hippocampus are vulnerable to aging, and age-related dysfunction in CA3 may be an early seed event closely linked to individual differences in memory decline. However, whether the differential vulnerability of CA3 and CA1 is associated with broader disruption in network-level functional interactions in relation to age-related memory impairment, and more specifically, whether CA3 dysconnectivity contributes to the effects of aging via CA1 network connectivity, has been difficult to test. Here, using resting-state fMRI in a group of aged rats uncontaminated by neurodegenerative disease, aged rats displayed widespread reductions in functional connectivity of CA3 and CA1 fields. Age-related memory deficits were predicted by connectivity between left CA3 and hippocampal circuitry along with connectivity between left CA1 and infralimbic prefrontal cortex. Notably, the effects of CA3 connectivity on memory performance were mediated by CA1 connectivity with prefrontal cortex. We additionally found that spatial learning and memory were associated with functional connectivity changes lateralized to the left CA3 and CA1 divisions. These results provide novel evidence that network-level dysfunction involving interactions of CA3 with CA1 is an early marker of poor cognitive outcome in aging.

Intrinsic differences in insular circuits moderate the negative association between nicotine dependence and cingulate-striatal connectivity strength.

The development of brain-based biomarkers to assess nicotine dependence severity and treatment efficacy are essential to improve the current marginally effective treatment outcomes. Cross-sectional resting state functional connectivity (rsFC) studies in humans identified a circuit between the dorsal anterior cingulate cortex and the ventral striatum that negatively correlated with increased nicotine dependence severity but was unaffected by acute nicotine administration, suggesting a trait marker of addiction. However, whether this trait circuit dysregulation is predispositional to or resultant from nicotine dependence is unclear. Using a rat model of nicotine dependence with longitudinal fMRI measurements, we assessed the relationship between ACC-striatal rsFC and nicotine dependence severity. Data-driven modularity-based parcellation of the rat medial prefrontal cortex (mPFC) combined with seed-based connectivity analysis with the striatum recapitulated the cingulate-striatum relationship observed in humans. Furthermore, the relationship between cingulate-striatal brain circuits and nicotine dependence severity as indexed by the intensity of precipitated withdrawal, was fully statistically moderated by a predispositional insular-frontal cortical functional circuit. These data suggest that the identified trans-species ACC-striatal circuit relationship with nicotine dependence severity is dysregulated following chronic nicotine administration-induced dependence and may be biased by individual differences in predispositional insula-based striatal-frontal circuits, highlighting the circuit's potential as a biomarker of dependence severity.

A Computational Framework for Dissociating Development-Related from Individually Variable Flexibility in Regional Modularity Assignment in Early Infancy.

Functional brain development in early infancy is a highly dynamic and complex process. Understanding each brain region's topological role and its development in the brain functional connectivity (FC) networks is essential for early disorder detection. A handful of previous studies have mostly focused on how FC network is changing regarding age. These approaches inevitably overlook the effect of individual variability for those at the same age that could shape unique cognitive capabilities and personalities among infants. With that in mind, we propose a novel computational framework based on across-subject across-age multilayer network analysis with a fully automatic (for parameter optimization), robust community detection algorithm. By detecting group consistent modules without losing individual information, this method allows a first-ever dissociation analysis of the two variability sources - age dependency and individual specificity - that greatly shape early brain development. This method is applied to a large cohort of 0-2 years old infants' functional MRI data during natural sleep. We not only detected the brain regions with greatest flexibility in this early developmental period but also identified five categories of brain regions with distinct development-related and individually variable flexibility changes. Our method is highly valuable for more thorough understanding of the early brain functional organizations and sheds light on early developmental abnormality detection.

Differential expression of nicotine withdrawal as a function of developmental age in the rat.

Cigarette smoking and resultant nicotine dependence remain major public health problems. Most smokers begin before the age of 18, yet preclinical models have insufficiently characterized the development of nicotine dependence in adolescence. To categorize the short-term effects of chronic nicotine administration throughout adolescence and adulthood, we exposed male Sprague Dawley rats to 14 days of continuously delivered nicotine (0, 1.2 or 4.8 mg/kg/d) using a subcutaneous osmotic minipump, starting between postnatal day 33 (p33) and p96. Next, to explore the effects of extended exposure to chronic nicotine, we exposed male Sprague Dawley rats to 42 days of continuous nicotine starting in adolescence (p33) or early adulthood (p68). Somatic and affective signs of precipitated withdrawal (PW) were observed after a mecamylamine (1.5 mg/kg, i.p.) challenge as compared to a saline injection. Short term nicotine exposure starting at p96, well within the adult period, elicited a significant increase in somatic PW as measured by a composite behavioral score. In contrast, adolescent exposure to nicotine elicited a unique behavioral profile, dependent on the starting age of exposure. Late adolescence exposure was characterized by scratching while adult exposure was characterized by facial tremors and yawns. Extended exposure to nicotine resulted in age specific characteristic nicotine withdrawal behaviors, including scratches, ptosis and locomotion, distinct from the short-term exposure. Thus, nicotine dependence severity, based on the expression of total somatic PW behaviors, is not observed until the adult period, and differences between adolescents and adults are observed using a more nuanced behavioral scoring approach. We conclude that age of nicotine initiation affects somatic withdrawal signs and their magnitude. These data serve as a foundation for understanding the underlying brain mechanisms of nicotine dependence and their development over adolescence and early adulthood.