The BF has been implicated in a variety of brain functions such as arousal, attention, and plasticity (Bakin and Weinberger, 1996; Brown et al., 2012; Everitt and Robbins, 1997; Froemke et al., 2013; Hasselmo and Sarter, 2011; Jones, 2011; Lin et al., 2015; Saper et al., 2010; Sarter et al., 2001). The dysfunction or loss of BF cholinergic neurons is an important feature of Alzheimer’s disease associated with cognitive impairment (Schliebs and Arendt, 2011; Whitehouse et al., 1982). In addition to forming extensive local synapses (Xu et al., 2015; Yang et al., 2014; Zaborszky and Duque, 2000), BF neurons receive inputs (Asanuma, 1989; Freund and Meskenaite, 1992; Grove, 1988a, Henny and Jones, 2006; Manns et al., 2001; Parent et al., 1988; Rye et al., 1984; Semba et al., 1988; Zaborszky and Cullinan, 1992) and send outputs (Cullinan and Zaborszky, 1991; Grove, 1988b; Jones and Cuello, 1989; Mesulam and Mufson, 1984; Paré and Smith, 1994) to many other brain areas (Steriade and McCarley, 2005; Zaborszky et al., 2012). However, how these long-range connections contribute to BF functions remains unclear.

An important challenge in understanding the function of the BF circuit is its neuronal heterogeneity. There are three major cell types spatially intermingled in the BF: cholinergic, glutamatergic, and GABAergic (Semba, 2000; Zaborszky et al., 2012). Selective lesion or pharmacological manipulation of the cholinergic system is well known to affect multiple brain functions (Wenk, 1997). For example, 192-IgG-saporin-mediated lesion of cholinergic neurons impaired the ability of rats to discriminate between signal and non-signal visual events in an attention task (McGaughy et al., 1996) and disrupted training-induced cortical map reorganization associated with motor learning (Conner et al., 2003). Glutamatergic and GABAergic BF neurons are also likely to serve important functions (Lin et al., 2015). For example, in recent studies the activity of non-cholinergic BF neurons was found to correlate with sustained attention (Hangya et al., 2015) or to encode reward and motivational salience information (Avila and Lin, 2014; Lin and Nicolelis, 2008; Nguyen and Lin, 2014), and optogenetic activation of PV+ GABAergic neurons was shown to regulate cortical gamma oscillations (Kim et al., 2015). In a study on sleep-wake control, cholinergic, glutamatergic, and PV+ neuron activity was found to promote wakefulness, while SOM+ neurons promoted sleep; these four cell types form extensive but highly specific local connections with each other for brain-state regulation (Xu et al., 2015). Thus, to understand the BF circuit function, it is crucial to map its inputs and outputs with cell-type specificity.

Most of the previous studies of BF long-range connections focused on specific regions connected to the BF, making it difficult to assess their whole-brain distribution. Recent advances in virus-assisted circuit tracing (Callaway and Luo, 2015; Huang and Zeng, 2013) and high-throughput imaging have greatly facilitated whole-brain mapping of long-range connectivity in a cell-type-specific manner (Oh et al., 2014; Osten and Margrie, 2013). In this study, we traced the long-range inputs and outputs of four genetically defined BF cell types. While the input distributions were similar across cell types, their output patterns showed striking differences. Our quantitative analysis of the whole-brain distributions of inputs and outputs for each BF cell type can serve as an anatomical blueprint for future studies of inter-regional pathways mediating BF functions.

Four Cre mouse lines were used to target different BF subpopulations for virus-mediated circuit tracing: choline acetyltransferase (ChAT)-Cre for cholinergic neurons, vesicular glutamate transporter 2 (VGLUT2)-Cre for glutamatergic neurons, and PV-Cre and SOM-Cre mice for two subtypes of GABAergic neurons. These four Cre lines have been shown to label largely non-overlapping BF neuron populations with high specificity (Xu et al., 2015).

To identify the long-range inputs to each cell type, we used RV-mediated transsynaptic retrograde tracing, which has been shown to label monosynaptic inputs to selected starter cells with high specificity (Miyamichi et al., 2011; Wall et al., 2013; Watabe-Uchida et al., 2012; Wickersham et al., 2007). First, we expressed avian-specific retroviral receptor (TVA), enhanced green fluorescent protein (eGFP), and rabies glycoprotein (RG) specifically in each cell type by injecting two Cre-inducible AAV vectors (AAV2-EF1α-FLEX-eGFP-2a-TVA and AAV2-EF1α-FLEX-RG) into the BF of ChAT-, VGLUT2-, PV-, or SOM-Cre mice (Figure 1A). The expression of RG was highly cell type specific and not detected in wild-type mice not expressing Cre recombinase (Figure 1—figure supplement 1). Two to three weeks later, we injected a modified RV (rabiesΔG-tdTomato+EnvA) that only infects cells expressing TVA, requires RG to spread retrogradely to presynaptic cells (Figure 1—figure supplement 2), and contains the tdTomato transgene. After histological sectioning and fluorescence imaging, each sample was aligned to a reference atlas (Allen Mouse Brain Atlas, see Materials and methods) to facilitate 3D whole-brain visualization and quantitative comparison across brain samples (Figure 1C). The starter cells (expressing both tdTomato and eGFP) and the transsynaptically labeled presynaptic neurons (expressing tdTomato only) were identified manually, and their locations were registered in the reference atlas (Figure 1—figure supplement 3).

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Brain samples were excluded from the analyses if very few input neurons (<200) were labeled in the whole brain. As noted in previous studies, due to the extremely efficient interaction between TVA and EnvA-pseudotyped rabies virus, the very low-level expression of TVA in non-Cre-expressing cells (not detectable based on fluorescent protein markers) allows the rabies virus to infect and label these cells with tdTomato at the injection site, independent of synaptic connections with starter cells (Beier et al., 2015; Menegas et al., 2015; Miyamichi et al., 2013; Ogawa et al., 2014; Pollak Dorocic et al., 2014; Wall et al., 2013; Watabe-Uchida et al., 2012; Weissbourd et al., 2014). However, this local contamination does not compromise the mapping of long-range inputs because RG (required for transsynaptic spread of RV) is not expressed in any non-Cre-expressing cells at sufficient levels for trans-complementation of rabiesΔG to allow transsynaptic spread of RV (Callaway and Luo, 2015; Miyamichi et al., 2013). To determine the spatial extent of the local contamination, we performed control experiments in the absence of RG and found very few non-specific labeling at >850 μm from the injection center (Figure 1—figure supplement 2D). Thus, presynaptic neurons were counted only in coronal sections outside of this range. While this procedure precludes identification of local inputs, synaptic interactions of the four cell types within the BF have been characterized electrophysiologically in a recent study (Xu et al., 2015). Another technical limitation of the study is that when the brain was removed for histological processing, the olfactory bulb was often damaged, which led to a significant underestimation of labeling (both the input neurons and axon projections) in the olfactory bulb.

We found 900 – 14,631 (median 7002) tdTomato-labeled presynaptic neurons in each brain (n = 17), and the convergence index (ratio between the number of input cells and starter cells) ranged between 4.3 and 77.7 (Figure 1—figure supplement 4). Such variability is comparable to that found in other studies using similar methods (Miyamichi et al., 2011; DeNardo et al., 2015). The presynaptic neurons were predominantly ipsilateral to the starter population (<5% contralateral) but were distributed in a large number of brain areas (Figure 2, Figure 3, Video 1). Since the number of labeled neurons varied across brain samples, and there was no significant difference between the four cell types (P = 0.27, one-way ANOVA), we normalized the data in each area by the total number of labeled neurons in each brain. When the brain was divided into 12 major regions (Figure 3A), the striatum and hypothalamus provided the highest numbers of inputs, while few labeled neurons were found in the medulla or cerebellum (Figure 3A).

Figure 2

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Figure 3

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Figure 3—source data 1

Distribution of input cells in 53 brain areas for ChAT+, VGLUT2+, PV+, and SOM+ BF neurons.

Shown are the mean ± s.e.m. of the percentage of inputs from each area for individual cell types. Note that within each of the 12 brain structures, there are unnamed sub-regions outside of the 53 areas listed in the table; thus the percentages of inputs in the listed areas do not always add up to the total percentage in the given brain region.

https://doi.org/10.7554/eLife.13214.009

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Video 1

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To facilitate data visualization at different levels of detail, we also used an interactive sunburst diagram (adapted from Allen Mouse Brain Atlas, http://www.brain-map.org/api/examples/examples/sunburst/) to represent the whole-brain distribution of inputs to each cell type (http://sleepcircuits.org/bf/). The brain structures are arranged hierarchically from inner to outer circles, and the size of each sector represents the percentage of input from the corresponding structure. The name of each structure and its input percentage can be read out by pointing the cursor, and each region of interest can be expanded with a mouse click.

When the input distribution was analyzed at a finer spatial scale (e.g., the 6th ring of the sunburst plot), the nucleus accumbens (Mesulam and Mufson, 1984; Zaborszky and Cullinan, 1992), lateral hypothalamus (Cullinan and Zaborszky, 1991; Grove, 1988b; Mesulam and Mufson, 1984), and central nucleus of the amygdala (Grove, 1988b; Pare and Smith, 1994) were among the structures containing the highest numbers of input neurons (Figure 2). Interestingly, many close neighbors of these densely labeled structures (e.g., the basolateral nucleus of the amygdala, immediately adjacent to the central nucleus) showed very sparse or no labeling, indicating high spatial specificity of the long-range inputs. On the other hand, the input distributions were qualitatively similar between cell types, although with quantitative differences. For example, glutamatergic neurons received significantly more inputs from the lateral hypothalamus than the other cell types (P = 0.001, VGLUT2 vs. ChAT; P = 0.001, VGLUT2 vs. PV; P = 0.001, VGLUT2 vs. SOM;, one-way ANOVA and post-hoc Tukey’s test), and PV+ neurons received more inputs from the nucleus accumbens (ACB) (P = 0.004, PV vs. ChAT; P = 0.003, PV vs. VGLUT2; P = 0.001, PV vs. SOM; one-way ANOVA and post-hoc Tukey’s test).

To further verify the inputs revealed by RV-mediated retrograde tracing, we optogenetically tested the synaptic connections from the prefrontal cortex (PFC) and ACB (Figure 4). To verify the innervation from PFC to BF cholinergic neurons, we injected AAV (AAV-DJ-CaMKIIα-hChR2-eYFP) expressing the mammalian codon-optimized channelrhodopsin-2 (hChR2) fused with enhanced yellow fluorescent protein (eYFP) in the orbital and agranular insular areas of the PFC (Figure 4—figure supplement 1) in ChAT-eGFP mice and made whole-cell voltage-clamp recordings from eGFP-labeled cholinergic neurons in acute BF slices (Figure 4A). Activating the ChR2-expressing axon terminals with blue light evoked excitatory responses in all recorded BF cholinergic neurons (n = 9, Figure 4B and C), confirming the input revealed with RV tracing. To confirm the innervation from ACB, we injected Cre-inducible AAV (AAV-DJ-EF1α-FLEX-ChR2-eYFP) expressing ChR2-eYFP in ACB of GAD2-Cre mice, made whole-cell current-clamp recordings from unlabeled postsynaptic BF neurons, and used single cell reverse-transcription PCR (RT-PCR) to identify the cell type. We found that all four BF cell types received inhibitory responses from the ACB (Figure 4D and E; ChAT+: 2 out 5 showed significant responses; VGLUT2+: 2/4; PV+: 3/3; SOM+: 4/8), which is consistent with the finding of an electron microscopic double-immunolabeling study performed in rats (Zaborszky and Cullinan, 1992).

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We next mapped the output of each BF cell type. To label the axonal projections, we injected AAV with Cre-dependent expression of mCherry (Figure 1B) into the BF of ChAT-, VGLUT2-, PV- or SOM-Cre mice. Two to three weeks after injection, the brain tissues were processed, images were registered to the reference atlas, and labeled axons were detected (Figure 1C, see Materials and methods). After the injection site (identified by the existence of labeled cell bodies) and locations with known major fiber tracks were excluded, the projection to each brain area was quantified by the number of pixels occupied by the detected axons (Oh et al., 2014) (see Materials and methods).

Parallel to the broad distribution of inputs (Figure 3), we found that each BF cell type also projected to a large number of brain areas (Figure 5, Figure 6, Video 2, http://sleepcircuits.org/bf/, >95% ipsilateral). Among the 12 major brain subdivisions (Figure 6A), the hypothalamus, pallidum, and striatum received the heaviest BF projections (Grove, 1988a; Henny and Jones, 2006), while very few axons were detected in the medulla or cerebellum (Figure 6A). Analysis at finer scales revealed high spatial specificity of the projections. For example, while several cell types projected strongly to the lateral habenula (Figure 5), few axons were detected in the immediately adjacent but anatomically distinct medial habenula (Hikosaka, 2010). In addition to providing extensive inputs to the BF (Figure 2), the lateral hypothalamus was also a major recipient of BF projections (Figure 5), indicating a strong BF-hypothalamus loop that may be important for brain-state regulation (Brown et al., 2012; Jones, 2011; Saper et al., 2010). Importantly, whereas the input distributions were generally similar across BF cell types (Figure 2, Figure 3), the output patterns showed striking differences. For example, compared to the other cell types, the projection from cholinergic neurons was much stronger in the basolateral amygdala, hippocampus, and visual cortex but much weaker in the lateral hypothalamus, lateral habenula, and the ventral tegmental area (Figure 5). The different projection patterns among cell types are also apparent in the 3D whole-brain view (Figure 6B, Figure 6—source data 1).

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Figure 6

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Figure 6—source data 1

Distribution of axonal projections to 53 brain areas from ChAT+, VGLUT2+, PV+, and SOM+ BF neurons.

Shown are the mean ± s.e.m. of the percentage of projections to each area for individual cell types.

https://doi.org/10.7554/eLife.13214.015

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To further compare the inputs and outputs between cell types, we averaged the spatial distributions across brain samples of each cell type and computed the correlation coefficient (CC) between cell types. For input distribution, the CCs between all cell types were high (Figure 7A), confirming their overall similarity observed earlier (Figure 2, Figure 3). On the other hand, when we computed the CCs between individual brain samples, we found higher CCs between samples of the same cell type (0.81 ± 0.04, s.e.m.) than of different cell types (0.70 ± 0.02, P = 0.01, t-test; Figure 7—figure supplement 1A). This indicates that despite the overall similarity, there were genuine differences between cell types that were beyond experimental variability.

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Figure 7—source data 1

Distribution of BF input and output from 12 major brain subdivisions across cell-type.

Shown are the mean ± s.e.m. of the inputs (A) and outputs (B) for each color-coded brain region, for individual cell types.

https://doi.org/10.7554/eLife.13214.018

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For output distribution, the CCs between individual samples of the same cell type were also high (0.86 ± 0.05; Figure 7—figure supplement 1B), indicating reproducibility of the mapping. However, most of the CCs between cell types (Figure 7B, computed after averaging across samples of the same cell type) were much lower than those for input distribution. The two lowest CCs (ChAT+ vs. VGLUT2+ and PV+ neurons) reflect the fact that while the cholinergic neurons project strongly to structures within the cerebral cortex (including olfactory areas, isocortex, hippocampus, and cortical subplate) and weakly to the brain stem structures (thalamus, hypothalamus, and midbrain), glutamatergic and PV+ neurons (with output distributions highly similar to each other) showed complementary projection patterns (Figure 6).

Finally, we computed the CC between the input and output distributions of each cell type (Figure 7C). The highest CC was found for SOM+ neurons, reflecting their strong reciprocal connections with a number of brain structures, including the hypothalamus, striatum, pallidum, and olfactory areas (Figure 7D, lower right, Figure 7—source data 1. For glutamatergic neurons, the high CC reflects their strong reciprocal connections with the hypothalamus and striatum (Figure 7D, upper right). For cholinergic and PV+ GABAergic neurons, the CCs between input and output distributions were much lower, reflecting the facts that while both cell types receive strong input from the striatum, cholinergic neurons project strongly to the cerebral cortex, and PV+ neurons to the pallidum and hypothalamus (Figure 7D, upper and lower left).

Using virus-mediated circuit mapping, we have characterized the whole-brain distributions of BF long-range connections, available in an open-access online database (http://sleepcircuits.org/bf/). Our experiments confirmed many previously demonstrated connections, but with cell-type specificity and quantitative analyses at multiple spatial scales. For example, we found that cortical inputs (Mesulam and Mufson, 1984; Steriade and McCarley, 2005) to all four BF cell types originate primarily from the agranular insular and orbital areas of the prefrontal cortex (Figure 2). While a previous ultrastructural study failed to detect convincing synaptic contact between prefrontal axons and BF cholinergic neurons (Zaborszky et al., 1997), our RV-mediated transsynaptic tracing demonstrated extensive monosynaptic innervation, which was also validated by electrophysiological recordings (Figure 4B and C). These findings have important implications on how the prefrontal cortex may exert top-down control of neural processing through its projection to the BF (Sarter et al., 2001). A recent study showed that cholinergic neurons in the BF are strongly activated by reinforcement signals during an auditory detection task (Hangya et al., 2015). Our whole-brain mapping of their inputs provides a list of candidate neurons through which the reinforcement signals are conveyed to the BF cholinergic neurons.

Regarding the outputs, we found striking differences across cell types (Figure 6). A recent study has shown that cholinergic, glutamatergic, and PV+ neurons all promote wakefulness, while SOM+ neurons promote sleep (Xu et al., 2015). The distinct projection patterns between cholinergic and glutamatergic/PV+ neurons (Figure 6) suggest that they preferentially regulate different brain functions during wakeful states. In a recent study, optogenetic activation of BF PV+ neurons was shown to enhance cortical gamma band oscillations (Kim et al., 2015). In addition to direct projections to the cortex, our study showed extensive subcortical projections of PV neurons, which may also contribute to the regulation of cortical gamma oscillations. The output distribution of SOM+ neurons, on the other hand, was highly correlated with the input distributions of all BF cell types (Figure 7C, bottom row); the broad GABAergic inhibition of these input areas by SOM+ neurons may be important for the sleep-promoting effect. Thus, while the highly convergent inputs from multiple brain areas allow a variety of sensory, motor, cognitive, and emotional signals to be integrated within the BF, the distinct projections by different cell types may enhance the versatility of the BF in coordinating diverse functions of multiple brain networks.

1. 1. Asanuma C

   (1989) Axonal arborizations of a magnocellular basal nucleus input and their relation to the neurons in the thalamic reticular nucleus of rats

   PNAS 86:4746–4750.

   https://doi.org/10.1073/pnas.86.12.4746

   • Google Scholar
2. 1. Avila I
   2. Lin SC

   (2014) Distinct neuronal populations in the basal forebrain encode motivational salience and movement

   Frontiers in Behavioral Neuroscience 8:421.

   https://doi.org/10.3389/fnbeh.2014.00421

   • Google Scholar
3. 1. Bakin JS
   2. Weinberger NM

   (1996) Induction of a physiological memory in the cerebral cortex by stimulation of the nucleus basalis

   PNAS 93:11219–11224.

   https://doi.org/10.1073/pnas.93.20.11219

   • Google Scholar
4. 1. Beier KT
   2. Steinberg EE
   3. DeLoach KE
   4. Xie S
   5. Miyamichi K
   6. Schwarz L
   7. Gao XJ
   8. Kremer EJ
   9. Malenka RC
   10. Luo L

   (2015) Circuit architecture of VTA dopamine neurons revealed by systematic input-output mapping

   Cell 162:622–634.

   https://doi.org/10.1016/j.cell.2015.07.015

   • Google Scholar
5. 1. Brown RE
   2. Basheer R
   3. McKenna JT
   4. Strecker RE
   5. McCarley RW

   (2012) Control of sleep and wakefulness

   Physiological Reviews 92:1087–1187.

   https://doi.org/10.1152/physrev.00032.2011

   • Google Scholar
6. 1. Callaway EM
   2. Luo L

   (2015) Monosynaptic circuit tracing with glycoprotein-deleted rabies viruses

   Journal of Neuroscience 35:8979–8985.

   https://doi.org/10.1523/JNEUROSCI.0409-15.2015

   • Google Scholar
7. 1. Conner JM
   2. Culberson A
   3. Packowski C
   4. Chiba AA
   5. Tuszynski MH

   (2003) Lesions of the Basal forebrain cholinergic system impair task acquisition and abolish cortical plasticity associated with motor skill learning

   Neuron 38:819–829.

   https://doi.org/10.1016/S0896-6273(03)00288-5

   • Google Scholar
8. 1. Cullinan WE
   2. Záborszky L

   (1991) Organization of ascending hypothalamic projections to the rostral forebrain with special reference to the innervation of cholinergic projection neurons

   Journal of Comparative Neurology 306:631–667.

   https://doi.org/10.1002/cne.903060408

   • Google Scholar
9. 1. DeNardo LA
   2. Berns DS
   3. DeLoach K
   4. Luo L

   (2015) Connectivity of mouse somatosensory and prefrontal cortex examined with trans-synaptic tracing

   Nature Neuroscience 18:1687–1697.

   https://doi.org/10.1038/nn.4131

   • Google Scholar
10. 1. Everitt BJ
    2. Robbins TW

    (1997) Central cholinergic systems and cognition

    Annual Review of Psychology 48:649–684.

    https://doi.org/10.1146/annurev.psych.48.1.649

    • Google Scholar
11. Book

    1. Franklin KBJ
    2. Paxinos G

    (2008)

    The Mouse Brain in Stereotaxic Coordinates Compact (3rd)

    Academic Press.

    • Google Scholar
12. 1. Freund TF
    2. Meskenaite V

    (1992) gamma-Aminobutyric acid-containing basal forebrain neurons innervate inhibitory interneurons in the neocortex

    PNAS 89:738–742.

    https://doi.org/10.1073/pnas.89.2.738

    • Google Scholar
13. 1. Froemke RC
    2. Carcea I
    3. Barker AJ
    4. Yuan K
    5. Seybold BA
    6. Martins AR
    7. Zaika N
    8. Bernstein H
    9. Wachs M
    10. Levis PA
    11. Polley DB
    12. Merzenich MM
    13. Schreiner CE

    (2013) Long-term modification of cortical synapses improves sensory perception

    Nature Neuroscience 16:79–88.

    https://doi.org/10.1038/nn.3274

    • Google Scholar
14. 1. Grove EA

    (1988) Efferent connections of the substantia innominata in the rat

    Journal of Comparative Neurology 277:347–364.

    https://doi.org/10.1002/cne.902770303

    • Google Scholar
15. 1. Grove EA

    (1988) Neural associations of the substantia innominata in the rat: Afferent connections

    Journal of Comparative Neurology 277:315–346.

    https://doi.org/10.1002/cne.902770302

    • Google Scholar
16. 1. Hangya B
    2. Ranade SP
    3. Lorenc M
    4. Kepecs A

    (2015) Central cholinergic neurons are rapidly recruited by reinforcement feedback

    Cell 162:1155–1168.

    https://doi.org/10.1016/j.cell.2015.07.057

    • Google Scholar
17. 1. Hasselmo ME
    2. Sarter M

    (2011) Modes and models of forebrain cholinergic neuromodulation of cognition

    Neuropsychopharmacology 36:52–73.

    https://doi.org/10.1038/npp.2010.104

    • Google Scholar
18. 1. Henny P
    2. Jones BE

    (2006) Vesicular glutamate (VGlut), GABA (VGAT), and acetylcholine (VACht) transporters in basal forebrain axon terminals innervating the lateral hypothalamus

    Journal of Comparative Neurology 496:453–467.

    https://doi.org/10.1002/cne.20928

    • Google Scholar
19. 1. Hikosaka O

    (2010) The habenula: from stress evasion to value-based decision-making

    Nature Reviews Neuroscience 11:503–513.

    https://doi.org/10.1038/nrn2866

    • Google Scholar
20. 1. Huang ZJ
    2. Zeng H

    (2013) Genetic approaches to neural circuits in the mouse

    Annual Review of Neuroscience 36:183–215.

    https://doi.org/10.1146/annurev-neuro-062012-170307

    • Google Scholar
21. 1. Jones BE
    2. Cuello AC

    (1989) Afferents to the basal forebrain cholinergic cell area from pontomesencephalic--catecholamine, serotonin, and acetylcholine--neurons

    Neuroscience 31:37–61.

    https://doi.org/10.1016/0306-4522(89)90029-8

    • Google Scholar
22. 1. Jones BE

    (2011) Neurobiology of waking and sleeping

    Handbook of Clinical Neurology 98:131–149.

    https://doi.org/10.1016/B978-0-444-52006-7.00009-5

    • Google Scholar
23. 1. Kim T
    2. Thankachan S
    3. McKenna JT
    4. McNally JM
    5. Yang C
    6. Choi JH
    7. Chen L
    8. Kocsis B
    9. Deisseroth K
    10. Strecker RE
    11. Basheer R
    12. Brown RE
    13. McCarley RW

    (2015) Cortically projecting basal forebrain parvalbumin neurons regulate cortical gamma band oscillations

    PNAS 112:3535–3540.

    https://doi.org/10.1073/pnas.1413625112

    • Google Scholar
24. 1. Lin SC
    2. Brown RE
    3. Hussain Shuler MG
    4. Petersen CC
    5. Kepecs A

    (2015) Optogenetic dissection of the basal forebrain neuromodulatory control of cortical activation, plasticity, and cognition

    Journal of Neuroscience 35:13896–13903.

    https://doi.org/10.1523/JNEUROSCI.2590-15.2015

    • Google Scholar
25. 1. Lin SC
    2. Nicolelis MA

    (2008) Neuronal ensemble bursting in the basal forebrain encodes salience irrespective of valence

    Neuron 59:138–149.

    https://doi.org/10.1016/j.neuron.2008.04.031

    • Google Scholar
26. 1. Manns ID
    2. Mainville L
    3. Jones BE

    (2001) Evidence for glutamate, in addition to acetylcholine and GABA, neurotransmitter synthesis in basal forebrain neurons projecting to the entorhinal cortex

    Neuroscience 107:249–263.

    https://doi.org/10.1016/S0306-4522(01)00302-5

    • Google Scholar
27. 1. McGaughy J
    2. Kaiser T
    3. Sarter M

    (1996) Behavioral vigilance following infusions of 192 IgG-saporin into the basal forebrain: selectivity of the behavioral impairment and relation to cortical AChE-positive fiber density

    Behavioral Neuroscience 110:247–265.

    https://doi.org/10.1037/0735-7044.110.2.247

    • Google Scholar
28. 1. Menegas W
    2. Bergan JF
    3. Ogawa SK
    4. Isogai Y
    5. Umadevi Venkataraju K
    6. Osten P
    7. Uchida N
    8. Watabe-Uchida M

    (2015) Dopamine neurons projecting to the posterior striatum form an anatomically distinct subclass

    eLife 4:e10032.

    https://doi.org/10.7554/eLife.10032

    • Google Scholar
29. 1. Mesulam MM
    2. Mufson EJ

    (1984) Neural inputs into the nucleus basalis of the substantia innominata (Ch4) in the rhesus monkey

    Brain 107 (Pt 1):253–274.

    https://doi.org/10.1093/brain/107.1.253

    • Google Scholar
30. 1. Miyamichi K
    2. Amat F
    3. Moussavi F
    4. Wang C
    5. Wickersham I
    6. Wall NR
    7. Taniguchi H
    8. Tasic B
    9. Huang ZJ
    10. He Z
    11. Callaway EM
    12. Horowitz MA
    13. Luo L

    (2011) Cortical representations of olfactory input by trans-synaptic tracing

    Nature 472:191–196.

    https://doi.org/10.1038/nature09714

    • Google Scholar
31. 1. Miyamichi K
    2. Shlomai-Fuchs Y
    3. Shu M
    4. Weissbourd BC
    5. Luo L
    6. Mizrahi A

    (2013) Dissecting local circuits: parvalbumin interneurons underlie broad feedback control of olfactory bulb output

    Neuron 80:1232–1245.

    https://doi.org/10.1016/j.neuron.2013.08.027

    • Google Scholar
32. 1. Narayan S
    2. Barnard RJ
    3. Young JA

    (2003) Two retroviral entry pathways distinguished by lipid raft association of the viral receptor and differences in viral infectivity

    Journal of Virology 77:1977–1983.

    https://doi.org/10.1128/JVI.77.3.1977-1983.2003

    • Google Scholar
33. 1. Nguyen DP
    2. Lin SC

    (2014) A frontal cortex event-related potential driven by the basal forebrain

    eLife 3:e02148.

    https://doi.org/10.7554/eLife.02148

    • Google Scholar
34. 1. Ogawa SK
    2. Cohen JY
    3. Hwang D
    4. Uchida N
    5. Watabe-Uchida M

    (2014) Organization of monosynaptic inputs to the serotonin and dopamine neuromodulatory systems

    Cell Reports 8:1105–1118.

    https://doi.org/10.1016/j.celrep.2014.06.042

    • Google Scholar
35. 1. Oh SW
    2. Harris JA
    3. Ng L
    4. Winslow B
    5. Cain N
    6. Mihalas S
    7. Wang Q
    8. Lau C
    9. Kuan L
    10. Henry AM
    11. Mortrud MT
    12. Ouellette B
    13. Nguyen TN
    14. Sorensen SA
    15. Slaughterbeck CR
    16. Wakeman W
    17. Li Y
    18. Feng D
    19. Ho A
    20. Nicholas E
    21. Hirokawa KE
    22. Bohn P
    23. Joines KM
    24. Peng H
    25. Hawrylycz MJ
    26. Phillips JW
    27. Hohmann JG
    28. Wohnoutka P
    29. Gerfen CR
    30. Koch C
    31. Bernard A
    32. Dang C
    33. Jones AR
    34. Zeng H

    (2014) A mesoscale connectome of the mouse brain

    Nature 508:207–214.

    https://doi.org/10.1038/nature13186

    • Google Scholar
36. 1. Osten P
    2. Margrie TW

    (2013) Mapping brain circuitry with a light microscope

    Nature Methods 10:515–523.

    https://doi.org/10.1038/nmeth.2477

    • Google Scholar
37. 1. Parent A
    2. Paré D
    3. Smith Y
    4. Steriade M

    (1988) Basal forebrain cholinergic and noncholinergic projections to the thalamus and brainstem in cats and monkeys

    Journal of Comparative Neurology 277:281–301.

    https://doi.org/10.1002/cne.902770209

    • Google Scholar
38. 1. Paré D
    2. Smith Y

    (1994) GABAergic projection from the intercalated cell masses of the amygdala to the basal forebrain in cats

    Journal of Comparative Neurology 344:33–49.

    https://doi.org/10.1002/cne.903440104

    • Google Scholar
39. 1. Pollak Dorocic I
    2. Fürth D
    3. Xuan Y
    4. Johansson Y
    5. Pozzi L
    6. Silberberg G
    7. Carlén M
    8. Meletis K

    (2014) A whole-brain atlas of inputs to serotonergic neurons of the dorsal and median raphe nuclei

    Neuron 83:663–678.

    https://doi.org/10.1016/j.neuron.2014.07.002

    • Google Scholar
40. 1. Rye DB
    2. Wainer BH
    3. Mesulam MM
    4. Mufson EJ
    5. Saper CB

    (1984) Cortical projections arising from the basal forebrain: a study of cholinergic and noncholinergic components employing combined retrograde tracing and immunohistochemical localization of choline acetyltransferase

    Neuroscience 13:627–643.

    https://doi.org/10.1016/0306-4522(84)90083-6

    • Google Scholar
41. 1. Saper CB
    2. Fuller PM
    3. Pedersen NP
    4. Lu J
    5. Scammell TE

    (2010) Sleep state switching

    Neuron 68:1023–1042.

    https://doi.org/10.1016/j.neuron.2010.11.032

    • Google Scholar
42. 1. Sarter M
    2. Givens B
    3. Bruno JP

    (2001) The cognitive neuroscience of sustained attention: where top-down meets bottom-up

    Brain Research Reviews 35:146–160.

    https://doi.org/10.1016/S0165-0173(01)00044-3

    • Google Scholar
43. 1. Schliebs R
    2. Arendt T

    (2011) The cholinergic system in aging and neuronal degeneration

    Behavioural Brain Research 221:555–563.

    https://doi.org/10.1016/j.bbr.2010.11.058

    • Google Scholar
44. 1. Semba K
    2. Reiner PB
    3. McGeer EG
    4. Fibiger HC

    (1988) Brainstem afferents to the magnocellular basal forebrain studied by axonal transport, immunohistochemistry, and electrophysiology in the rat

    Journal of Comparative Neurology 267:433–453.

    https://doi.org/10.1002/cne.902670311

    • Google Scholar
45. 1. Semba K

    (2000) Multiple output pathways of the basal forebrain: organization, chemical heterogeneity, and roles in vigilance

    Behavioural Brain Research 115:117–141.

    https://doi.org/10.1016/S0166-4328(00)00254-0

    • Google Scholar
46. Book

    1. Steriade M
    2. McCarley RW

    (2005) Brain Control of Wakefulness and Sleep (2nd ed)

    New York: Springer.

    https://doi.org/10.1007/b102230

    • Google Scholar
47. 1. Wall NR
    2. De La Parra M
    3. Callaway EM
    4. Kreitzer AC

    (2013) Differential innervation of direct- and indirect-pathway striatal projection neurons

    Neuron 79:347–360.

    https://doi.org/10.1016/j.neuron.2013.05.014

    • Google Scholar
48. 1. Watabe-Uchida M
    2. Zhu L
    3. Ogawa SK
    4. Vamanrao A
    5. Uchida N

    (2012) Whole-brain mapping of direct inputs to midbrain dopamine neurons

    Neuron 74:858–873.

    https://doi.org/10.1016/j.neuron.2012.03.017

    • Google Scholar
49. 1. Weissbourd B
    2. Ren J
    3. DeLoach KE
    4. Guenthner CJ
    5. Miyamichi K
    6. Luo L

    (2014) Presynaptic partners of dorsal raphe serotonergic and GABAergic neurons

    Neuron 83:645–662.

    https://doi.org/10.1016/j.neuron.2014.06.024

    • Google Scholar
50. 1. Wenk GL

    (1997) The nucleus basalis magnocellularis cholinergic system: one hundred years of progress

    Neurobiology of Learning and Memory 67:85–95.

    https://doi.org/10.1006/nlme.1996.3757

    • Google Scholar
51. 1. Whitehouse PJ
    2. Price DL
    3. Struble RG
    4. Clark AW
    5. Coyle JT
    6. Delon MR

    (1982) Alzheimer's disease and senile dementia: loss of neurons in the basal forebrain

    Science 215:1237–1239.

    https://doi.org/10.1126/science.7058341

    • Google Scholar
52. 1. Wickersham IR
    2. Lyon DC
    3. Barnard RJ
    4. Mori T
    5. Finke S
    6. Conzelmann KK
    7. Young JA
    8. Callaway EM

    (2007) Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons

    Neuron 53:639–647.

    https://doi.org/10.1016/j.neuron.2007.01.033

    • Google Scholar
53. 1. Xu M
    2. Chung S
    3. Zhang S
    4. Zhong P
    5. Ma C
    6. Chang WC
    7. Weissbourd B
    8. Sakai N
    9. Luo L
    10. Nishino S
    11. Dan Y

    (2015) Basal forebrain circuit for sleep-wake control

    Nature Neuroscience 18:1641–1647.

    https://doi.org/10.1038/nn.4143

    • Google Scholar
54. 1. Yang C
    2. McKenna JT
    3. Zant JC
    4. Winston S
    5. Basheer R
    6. Brown RE

    (2014) Cholinergic neurons excite cortically projecting basal forebrain GABAergic neurons

    Journal of Neuroscience 34:2832–2844.

    https://doi.org/10.1523/JNEUROSCI.3235-13.2014

    • Google Scholar
55. 1. Zaborszky L
    2. Cullinan WE

    (1992) Projections from the nucleus accumbens to cholinergic neurons of the ventral pallidum: a correlated light and electron microscopic double-immunolabeling study in rat

    Brain Research 570:92–101.

    https://doi.org/10.1016/0006-8993(92)90568-T

    • Google Scholar
56. 1. Zaborszky L
    2. Duque A

    (2000) Local synaptic connections of basal forebrain neurons

    Behavioural Brain Research 115:143–158.

    https://doi.org/10.1016/S0166-4328(00)00255-2

    • Google Scholar
57. 1. Zaborszky L
    2. Gaykema RP
    3. Swanson DJ
    4. Cullinan WE

    (1997) Cortical input to the basal forebrain

    Neuroscience 79:1051–1078.

    https://doi.org/10.1016/S0306-4522(97)00049-3

    • Google Scholar
58. 1. Zaborszky L
    2. van den Pol A
    3. Gyengesi E

    (2012)

    Mouse Nervous System

    684–718, The basal forebrain cholinergic projection system in mice, Mouse Nervous System, 10.1016/B978-0-12-369497-3.10028-7.

    • Google Scholar

Author details

1. Johnny Phong Do

   Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, United States

   Contribution

   JPD, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Min Xu and Seung-Hee Lee

   Competing interests

   No competing interests declared.

2. Min Xu

   Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, United States

   Contribution

   MX, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Johnny Phong Do and Seung-Hee Lee

   Competing interests

   No competing interests declared.

3. Seung-Hee Lee

   Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, United States

   Present address

   Korea Advanced Institute of Science and Technology, Republic of Korea

   Contribution

   S-HL, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Johnny Phong Do and Min Xu

   Competing interests

   No competing interests declared.

   "This ORCID iD identifies the author of this article:" 0000-0002-9486-5771

4. Wei-Cheng Chang

   Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, United States

   Contribution

   W-CC, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

5. Siyu Zhang

   Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, United States

   Contribution

   SZ, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

6. Shinjae Chung

   Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, United States

   Contribution

   SC, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

7. Tyler J Yung

   Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, United States

   Contribution

   TJY, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

   "This ORCID iD identifies the author of this article:" 0000-0002-8361-837X

8. Jiang Lan Fan

   Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, United States

   Contribution

   JLF, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

9. Kazunari Miyamichi

   Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, United States

   Present address

   Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan

   Contribution

   KM, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   No competing interests declared.

10. Liqun Luo

    Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, United States

    Contribution

    LL, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    LL: Reviewing editor, eLife

11. Yang Dan

    Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, United States

    Contribution

    YD, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    ydan@berkeley.edu

    Competing interests

    No competing interests declared.

    "This ORCID iD identifies the author of this article:" 0000-0002-3818-877X

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