Punishment resistance for cocaine is associated with inflexible habits in rats.

Addiction is characterized by continued drug use despite negative consequences. In an animal model, a subset of rats continues to self-administer cocaine despite footshock consequences, showing punishment resistance. We sought to test the hypothesis that punishment resistance arises from failure to exert goal-directed control over habitual cocaine seeking. While habits are not inherently permanent or maladaptive, continued use of habits under conditions that should encourage goal-directed control makes them maladaptive and inflexible. We trained male and female Sprague Dawley rats on a seeking-taking chained schedule of cocaine self-administration. We then exposed them to four days of punishment testing in which footshock was delivered randomly on one-third of trials. Before and after punishment testing (four days pre-punishment and ≥ four days post-punishment), we assessed whether cocaine seeking was goal-directed or habitual using outcome devaluation via cocaine satiety. We found that punishment resistance was associated with continued use of habits, whereas punishment sensitivity was associated with increased goal-directed control. Although punishment resistance for cocaine was not predicted by habitual responding pre-punishment, it was associated with habitual responding post-punishment. In parallel studies of food self-administration, we similarly observed that punishment resistance was associated with habitual responding post-punishment but not pre-punishment in males, although it was associated with habitual responding both pre- and post-punishment in females, indicating that punishment resistance was predicted by habitual responding in food-seeking females. These findings indicate that punishment resistance is related to habits that have become inflexible and persist under conditions that should encourage a transition to goal-directed behavior.

Computational EEG attributes predict response to therapy for epileptic spasms.

OBJECTIVE: We set out to evaluate whether response to treatment for epileptic spasms is associated with specific candidate computational EEG biomarkers, independent of clinical attributes. METHODS: We identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs). To evaluate the extent to which each feature is independently associated with response and relapse, we conducted logistic and proportional hazards regression, respectively. RESULTS: After statistical adjustment for the duration of epileptic spasms prior to treatment, we observed an association between response and stronger baseline and post-treatment LRTCs (P = 0.042 and P = 0.004, respectively), and higher post-treatment entropy (P = 0.003). On an exploratory basis, freedom from relapse was associated with stronger post-treatment LRTCs (P = 0.006) and higher post-treatment entropy (P = 0.044). CONCLUSION: This study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse. SIGNIFICANCE: This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.

Evaluating Injury Severity in Neonatal Encephalopathy Using Automated Quantitative Electroencephalography Analysis: A Pilot Study.

Quantitative analysis of electroencephalography (qEEG) is a potential source of biomarkers for neonatal encephalopathy (NE). However, prior studies using qEEG in NE were limited in their generalizability due to individualized techniques for calculating qEEG features or labor-intensive pre-selection of EEG data. We piloted a fully automated method using commercially available software to calculate the suppression ratio (SR), absolute delta power, and relative delta, theta, alpha, and beta power from EEG of neonates undergoing 72 h of therapeutic hypothermia (TH) for NE between April 20, 2018, and November 4, 2019. We investigated the association of qEEG with degree of encephalopathy (modified Sarnat score), severity of neuroimaging abnormalities following TH (National Institutes of Child Health and Development Neonatal Research Network [NICHD-NRN] score), and presence of seizures. Thirty out of 38 patients met inclusion criteria. A more severe modified Sarnat score was associated with higher SR during all phases of TH, lower absolute delta power during all phases except rewarming, and lower relative delta power during the last 24 h of TH. In 21 patients with neuroimaging data, a worse NICHD-NRN score was associated with higher SR, lower absolute delta power, and higher relative beta power during all phases. QEEG features were not significantly associated with the presence of seizures after correction for multiple comparisons. Our results are consistent with those of prior studies using qEEG in NE and support automated qEEG analysis as an accessible, generalizable method for generating biomarkers of NE and response to TH. Additionally, we found evidence of an immature relative frequency composition in neonates with more severe brain injury, suggesting that automated qEEG analysis may have a use in the assessment of brain maturity.

Making and breaking habits: Revisiting the definitions and behavioral factors that influence habits in animals.

Habits have garnered significant interest in studies of associative learning and maladaptive behavior. However, habit research has faced scrutiny and challenges related to the definitions and methods. Differences in the conceptualizations of habits between animal and human studies create difficulties for translational research. Here, we review the definitions and commonly used methods for studying habits in animals and humans and discuss potential alternative ways to assess habits, such as automaticity. To better understand habits, we then focus on the behavioral factors that have been shown to make or break habits in animals, as well as potential mechanisms underlying the influence of these factors. We discuss the evidence that habitual and goal-directed systems learn in parallel and that they seem to interact in competitive and cooperative manners. Finally, we draw parallels between habitual responding and compulsive drug seeking in animals to delineate the similarities and differences in these behaviors.

Drug reinforcement impairs cognitive flexibility by inhibiting striatal cholinergic neurons.

Addictive substance use impairs cognitive flexibility, with unclear underlying mechanisms. The reinforcement of substance use is mediated by the striatal direct-pathway medium spiny neurons (dMSNs) that project to the substantia nigra pars reticulata (SNr). Cognitive flexibility is mediated by striatal cholinergic interneurons (CINs), which receive extensive striatal inhibition. Here, we hypothesized that increased dMSN activity induced by substance use inhibits CINs, reducing cognitive flexibility. We found that cocaine administration in rodents caused long-lasting potentiation of local inhibitory dMSN-to-CIN transmission and decreased CIN firing in the dorsomedial striatum (DMS), a brain region critical for cognitive flexibility. Moreover, chemogenetic and time-locked optogenetic inhibition of DMS CINs suppressed flexibility of goal-directed behavior in instrumental reversal learning tasks. Notably, rabies-mediated tracing and physiological studies showed that SNr-projecting dMSNs, which mediate reinforcement, sent axonal collaterals to inhibit DMS CINs, which mediate flexibility. Our findings demonstrate that the local inhibitory dMSN-to-CIN circuit mediates the reinforcement-induced deficits in cognitive flexibility.

Punishment resistance for cocaine is associated with inflexible habits in rats.

Addiction is characterized by continued drug use despite negative consequences. In an animal model, a subset of rats continues to self-administer cocaine despite footshock consequences, showing punishment resistance. We sought to test the hypothesis that punishment resistance arises from failure to exert goal-directed control over habitual cocaine seeking. While habits are not inherently permanent or maladaptive, continued use of habits under conditions that should encourage goal-directed control makes them maladaptive and inflexible. We trained male and female Sprague Dawley rats on a seeking-taking chained schedule of cocaine self-administration (2 h/day). We then exposed them to 4 days of punishment testing, in which footshock (0.4 mA, 0.3 s) was delivered randomly on one-third of trials, immediately following completion of seeking and prior to extension of the taking lever. Before and after punishment testing (4 days pre-punishment and ≥4 days post-punishment), we assessed whether cocaine seeking was goal-directed or habitual using outcome devaluation via cocaine satiety. We found that punishment resistance was associated with continued use of habits, whereas punishment sensitivity was associated with increased goal-directed control. Although punishment resistance was not predicted by habitual responding pre-punishment, it was associated with habitual responding post-punishment. In parallel studies of food self-administration, we similarly observed that punishment resistance was associated with habitual responding post-punishment but not pre-punishment. These findings indicate that punishment resistance is related to habits that have become inflexible and persist under conditions that should encourage a transition to goal-directed behavior.

Stimulation to probe, excite, and inhibit the epileptic brain.

Direct cortical stimulation has been applied in epilepsy for nearly a century and has experienced a renaissance, given unprecedented opportunities to probe, excite, and inhibit the human brain. Evidence suggests stimulation can increase diagnostic and therapeutic utility in patients with drug-resistant epilepsies. However, choosing appropriate stimulation parameters is not a trivial issue, and is further complicated by epilepsy being characterized by complex brain state dynamics. In this article derived from discussions at the ICTALS 2022 Conference (International Conference on Technology and Analysis for Seizures), we succinctly review the literature on cortical stimulation applied acutely and chronically to the epileptic brain for localization, monitoring, and therapeutic purposes. In particular, we discuss how stimulation is used to probe brain excitability, discuss evidence on the usefulness of stimulation to trigger and stop seizures, review therapeutic applications of stimulation, and finally discuss how stimulation parameters are impacted by brain dynamics. Although research has advanced considerably over the past decade, there are still significant hurdles to optimizing use of this technique. For example, it remains unclear to what extent short timescale diagnostic biomarkers can predict long-term outcomes and to what extent these biomarkers add information to already existing biomarkers from passive electroencephalographic recordings. Further questions include the extent to which closed loop stimulation offers advantages over open loop stimulation, what the optimal closed loop timescales may be, and whether biomarker-informed stimulation can lead to seizure freedom. The ultimate goal of bioelectronic medicine remains not just to stop seizures but rather to cure epilepsy and its comorbidities.

Towards optimizing single pulse electrical stimulation: High current intensity, short pulse width stimulation most effectively elicits evoked potentials.

BACKGROUND: While single pulse electrical stimulation (SPES) is increasingly used to study effective connectivity, the effects of varying stimulation parameters on the resulting cortico-cortical evoked potentials (CCEPs) have not been systematically explored. OBJECTIVE: We sought to understand the interacting effects of stimulation pulse width, current intensity, and charge on CCEPs through an extensive testing of this parameter space and analysis of several response metrics. METHODS: We conducted SPES in 11 patients undergoing intracranial EEG monitoring using five combinations of current intensity (1.5, 2.0, 3.0, 5.0, and 7.5 mA) and pulse width at each of three charges (0.750, 1.125, and 1.500 µC/phase) to study how CCEP amplitude, distribution, latency, morphology, and stimulus artifact amplitude vary with each parameter. RESULTS: Stimulations with a greater charge or a greater current intensity and shorter pulse width at a given charge generally resulted in greater CCEP amplitudes and spatial distributions, shorter latencies, and increased waveform correlation. These effects interacted such that stimulations with the lowest charge and highest current intensities resulted in greater response amplitudes and spatial distributions than stimulations with the highest charge and lowest current intensities. Stimulus artifact amplitude increased with charge, but this could be mitigated by using shorter pulse widths. CONCLUSIONS: Our results indicate that individual combinations of current intensity and pulse width, in addition to charge, are important determinants of CCEP magnitude, morphology, and spatial extent. Together, these findings suggest that high current intensity, short pulse width stimulations are optimal SPES settings for eliciting strong and consistent responses while minimizing charge.

An atypically mild case of ethylmalonic encephalopathy with pathogenic ETHE1 variant.

Ethylmalonic encephalopathy (EE) is a rare, severe, autosomal recessive condition caused by pathogenic variants in ETHE1 leading to progressive encephalopathy, hypotonia evolving to dystonia, petechiae, orthostatic acrocyanosis, diarrhea, and elevated ethylmalonic acid in urine. In this case report, we describe a patient with only mild speech and gross motor delays, subtle biochemical abnormalities, and normal brain imaging found to be homozygous for a pathogenic ETHE1 variant (c.586G>A) via whole exome sequencing. This case highlights the clinical heterogeneity of ETHE1 mutations and the utility of whole-exome sequencing in diagnosing mild cases of EE.

Quantitative approaches to guide epilepsy surgery from intracranial EEG.

Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.

Cortico-cortical evoked potentials in response to varying stimulation intensity improves seizure localization.

OBJECTIVE: As single pulse electrical stimulation (SPES) is increasingly utilized to help localize the seizure onset zone (SOZ), it is important to understand how stimulation intensity can affect the ability to use cortico-cortical evoked potentials (CCEPs) to delineate epileptogenic regions. METHODS: We studied 15 drug-resistant epilepsy patients undergoing intracranial EEG monitoring and SPES with titrations of stimulation intensity. The N1 amplitude and distribution of CCEPs elicited in the SOZ and non-seizure onset zone (nSOZ) were quantified at each intensity. The separability of the SOZ and nSOZ using N1 amplitudes was compared between models using responses to titrations, responses to one maximal intensity, or both. RESULTS: At 2 mA and above, the increase in N1 amplitude with current intensity was greater for responses within the SOZ, and SOZ response distribution was maximized by 4-6 mA. Models incorporating titrations achieved better separability of SOZ and nSOZ compared to those using one maximal intensity. CONCLUSIONS: We demonstrated that differences in CCEP amplitude over a range of current intensities can improve discriminability of SOZ regions. SIGNIFICANCE: This study provides insight into the underlying excitability of the SOZ and how differences in current-dependent amplitudes of CCEPs may be used to help localize epileptogenic sites.

Source-sink connectivity: a novel interictal EEG marker for seizure localization.

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.

Stimulating native seizures with neural resonance: a new approach to localize the seizure onset zone.

Successful outcomes in epilepsy surgery rely on the accurate localization of the seizure onset zone. Localizing the seizure onset zone is often a costly and time-consuming process wherein a patient undergoes intracranial EEG monitoring, and a team of clinicians wait for seizures to occur. Clinicians then analyse the intracranial EEG before each seizure onset to identify the seizure onset zone and localization accuracy increases when more seizures are captured. In this study, we develop a new approach to guide clinicians to actively elicit seizures with electrical stimulation. We propose that a brain region belongs to the seizure onset zone if a periodic stimulation at a particular frequency produces large amplitude oscillations in the intracranial EEG network that propagate seizure activity. Such responses occur when there is 'resonance' in the intracranial EEG network, and the resonant frequency can be detected by observing a sharp peak in the magnitude versus frequency response curve, called a Bode plot. To test our hypothesis, we analysed single-pulse electrical stimulation response data in 32 epilepsy patients undergoing intracranial EEG monitoring. For each patient and each stimulated brain region, we constructed a Bode plot by estimating a transfer function model from the intracranial EEG 'impulse' or single-pulse electrical stimulation response. The Bode plots were then analysed for evidence of resonance. First, we showed that when Bode plot features were used as a marker of the seizure onset zone, it distinguished successful from failed surgical outcomes with an area under the curve of 0.83, an accuracy that surpassed current methods of analysis with cortico-cortical evoked potential amplitude and cortico-cortical spectral responses. Then, we retrospectively showed that three out of five native seizures accidentally triggered in four patients during routine periodic stimulation at a given frequency corresponded to a resonant peak in the Bode plot. Last, we prospectively stimulated peak resonant frequencies gleaned from the Bode plots to elicit seizures in six patients, and this resulted in an induction of three seizures and three auras in these patients. These findings suggest neural resonance as a new biomarker of the seizure onset zone that can guide clinicians in eliciting native seizures to more quickly and accurately localize the seizure onset zone.

New directions in modelling dysregulated reward seeking for food and drugs.

Behavioral models are central to behavioral neuroscience. To study the neural mechanisms of maladaptive behaviors (including binge eating and drug addiction), it is essential to develop and utilize appropriate animal models that specifically focus on dysregulated reward seeking. Both food and cocaine are typically consumed in a regulated manner by rodents, motivated by reward and homeostatic mechanisms. However, both food and cocaine seeking can become dysregulated, resulting in binge-like consumption and compulsive patterns of intake. The speakers in this symposium for the 2021 International Behavioral Neuroscience Meeting utilize behavioral models of dysregulated reward-seeking to investigate the neural mechanisms of binge-like consumption, enhanced cue-driven reward seeking, excessive motivation, and continued use despite negative consequences. In this review, we outline examples of maladaptive patterns of intake and explore recent animal models that drive behavior to become dysregulated, including stress exposure and intermittent access to rewards. Lastly, we explore select behavioral and neural mechanisms underlying dysregulated reward-seeking for both food and drugs.

Chronic alcohol drinking persistently suppresses thalamostriatal excitation of cholinergic neurons to impair cognitive flexibility.

Exposure to addictive substances impairs flexible decision making. Cognitive flexibility is mediated by striatal cholinergic interneurons (CINs). However, how chronic alcohol drinking alters cognitive flexibility through CINs remains unclear. Here, we report that chronic alcohol consumption and withdrawal impaired reversal of instrumental learning. Chronic alcohol consumption and withdrawal also caused a long-lasting (21 days) reduction of excitatory thalamic inputs onto CINs and reduced pause responses of CINs in the dorsomedial striatum (DMS). CINs are known to inhibit glutamatergic transmission in dopamine D1 receptor-expressing medium spiny neurons (D1-MSNs) but facilitate this transmission in D2-MSNs, which may contribute to flexible behavior. We discovered that chronic alcohol drinking impaired CIN-mediated inhibition in D1-MSNs and facilitation in D2-MSNs. Importantly, in vivo optogenetic induction of long-term potentiation of thalamostriatal transmission in DMS CINs rescued alcohol-induced reversal learning deficits. These results demonstrate that chronic alcohol drinking reduces thalamic excitation of DMS CINs, compromising their regulation of glutamatergic transmission in MSNs, which may contribute to alcohol-induced impairment of cognitive flexibility. These findings provide a neural mechanism underlying inflexible drinking in alcohol use disorder.

Discriminating goal-directed and habitual cocaine seeking in rats using a novel outcome devaluation procedure.

Habits are theorized to play a key role in compulsive cocaine seeking, yet there is limited methodology for assessing habitual responding for intravenous (IV) cocaine. We developed a novel outcome devaluation procedure to discriminate goal-directed from habitual responding in cocaine-seeking rats. This procedure elicits devaluation temporarily and requires no additional training, allowing repeated testing at different time points. After training male rats to self-administer IV cocaine, we devalued the drug outcome via experimenter-administered IV cocaine (a "satiety" procedure) prior to a 10-min extinction test. Many rats were sensitive to outcome devaluation, a hallmark of goal-directed responding. These animals reduced responding when given a dose of experimenter-administered cocaine that matched or exceeded satiety levels during self-administration. However, other rats were insensitive to experimenter-administered cocaine, suggesting their responding was habitual. Importantly, reinforcement schedules and neural manipulations that produce goal-directed responding (i.e., ratio schedules or dorsolateral striatum lesions) caused sensitivity to outcome devaluation, whereas reinforcement schedules and neural manipulations that produce habitual responding (i.e., interval schedules or dorsomedial striatum lesions) caused insensitivity. Satiety-based outcome devaluation is an innovative new tool to dissect the neural and behavioral mechanisms underlying IV cocaine-seeking behavior.

Effects of stimulation intensity on intracranial cortico-cortical evoked potentials: A titration study.

OBJECTIVE: The aim of the present study was to investigate the optimal stimulation parameters for eliciting cortico-cortical evoked potentials (CCEPs) for mapping functional and epileptogenic networks. METHODS: We studied 13 patients with refractory epilepsy undergoing intracranial EEG monitoring. We systematically titrated the intensity of single-pulse electrical stimulation at multiple sites to assess the effect of increasing current on salient features of CCEPs such as N1 potential magnitude, signal to noise ratio, waveform similarity, and spatial distribution of responses. Responses at each incremental stimulation setting were compared to each other and to a final set of responses at the maximum intensity used in each patient (3.5-10 mA, median 6 mA). RESULTS: We found that with a biphasic 0.15 ms/phase pulse at least 2-4 mA is needed to differentiate between non-responsive and responsive sites, and that stimulation currents of 6-7 mA are needed to maximize amplitude and spatial distribution of N1 responses and stabilize waveform morphology. CONCLUSIONS: We determined a minimum stimulation threshold necessary for eliciting CCEPs, as well as a point at which the current-dependent relationship of several response metrics all saturate. SIGNIFICANCE: This titration study provides practical, immediate guidance on optimal stimulation parameters to study specific features of CCEPs, which have been increasingly used to map both functional and epileptic brain networks in humans.

Computational characteristics of interictal EEG as objective markers of epileptic spasms.

OBJECTIVE: Favorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five categories of computational electroencephalographic (EEG) measures as markers of ES. METHODS: We measured 1) amplitude, 2) power spectra, 3) Shannon entropy and permutation entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity using cross-correlation and phase lag index (PLI). EEG data were analyzed from ES patients (n = 40 patients) and healthy controls (n = 20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient. RESULTS: In ES patients, EEG amplitude was significantly higher in all electrodes when compared to controls. Shannon and permutation entropy were lower in ES patients than control subjects. The DFA intercept values in ES patients were significantly higher than control subjects, while DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls when based on both cross-correlation and PLI. Significance for all statistical tests was p < 0.05, adjusted for multiple comparisons using the Benjamini-Hochberg procedure as appropriate. Finally, using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96). CONCLUSIONS: Computational EEG features successfully distinguish ES patients from controls in a large, blinded study. SIGNIFICANCE: These objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.

Infant functional networks are modulated by state of consciousness and circadian rhythm.

Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

Monosynaptic Retrograde Tracing From Prelimbic Neuron Subpopulations Projecting to Either Nucleus Accumbens Core or Rostromedial Tegmental Nucleus.

The prelimbic (PL) region of the medial prefrontal cortex (mPFC) has been implicated in both driving and suppressing motivated behaviors, including cocaine-seeking in rats. These seemingly opposing functions may be mediated by different efferent targets of PL projections, such as the nucleus accumbens (NAc) core and rostromedial tegmental nucleus (RMTg), which have contrasting roles in reward-seeking behaviors. We sought to characterize the anatomical connectivity differences between PL neurons projecting to NAc core and RMTg. We used conventional retrograde tracers to reveal distinct subpopulations of PL neurons projecting to NAc core vs. RMTg in rats, with very little overlap. To examine potential differences in input specificity for these two PL subpopulations, we then used Cre-dependent rabies virus (EnvA-RV-EGFP) as a monosynaptic retrograde tracer and targeted specific PL neurons via injections of retrograde CAV2-Cre in either NAc core or RMTg. We observed a similar catalog of cortical, thalamic, and limbic afferents for both NAc- and RMTg-projecting populations, with the primary source of afferent information arising from neighboring prefrontal neurons in ipsilateral PL and infralimbic cortex (IL). However, when the two subpopulations were directly compared, we found that RMTg-projecting PL neurons received a greater proportion of input from ipsilateral PL and IL, whereas NAc-projecting PL neurons received a greater proportion of input from most other cortical areas, mediodorsal thalamic nucleus, and several other subcortical areas. NAc-projecting PL neurons also received a greater proportion of contralateral cortical input. Our findings reveal that PL subpopulations differ not only in their efferent target but also in the input specificity from afferent structures. These differences in connectivity are likely to be critical to functional differences of PL subpopulations.