A possible explanation for increased FA could be the variety of neurogenic properties of nicotine. In addition to maintaining and reinforcing smoking behavior, nicotine is reported to have other properties, such as anxiolytic properties and learning and memory-enhancing properties. Despite the proposal that chronic nicotine exposure may ultimately bring no benefits on mood and cognition, nicotine per se is known to be a neuroprotective agent, and prevents arachidonic acid induced injury to neurons and apoptotic cell death. Also, previous studies have revealed that nicotine upregulates calcium binding proteins, increases the levels of intracellular calcium measured and stimulates nerve growth factor, which could also be neuroprotective. These previous reported neuropro tective effects could be consistent with increased FA from chronic cigarette smoking. However, increased FA in white matter of brain in chronic cigarette smokers may not be beneficial. For example, Hoeft et al reported that increased FA of right superior longitudinal fasciculus in Williams syndrome individuals was associated with deficits in visuospatial construction. Similarly, a study of attention deficit hyperactivity disorder also found a correlation between increased FA with deficits in cognitive function. Increased FA is also reported in euthymic bipolar patients. Furthermore, evidence from previous studies reveals that increased FA could be a marker of acute inflammatory processes affecting neural tissue, indicating greater inflammation or less myelination. Thus,grow tray stand our result of increased FA in white matter might be associated with inflammatory changes and axonal damage in fronto-parietal cortex in chronic cigarette smokers.

An alternate interpretation for increased FA in chronic cigarette smoking and some psychiatric disorders could be that they reflect the compensatory mechanisms and could be the result of increases in local white matter density. The higher FA found here is consistent with another study using DTI in 10 chronic cigarette smokers. However, we did not replicate their finding of increased FA in the body and whole corpus callosum in chronic cigarette smokers. Also, a recent study found that both prenatal exposure and adolescent exposure to tobacco smoke were associated with increased FA in anterior cortical white matter.Gazdzinski et al, examined the impact of smoking on alcohol-dependent individuals and found that the combination of cigarette smoking and alcohol dependence results in significantly larger volumes of temporal and frontal white matter; recently, they further confirmed the increased FA result in a abstinent smoking and non-smoking alcoholics study. However, Gons RA et al studied 503 small-vessel disease subjects aged between 50–85 years and found that cigarette smoking is associated with the reduction of FA in cerebral white matter. Age, use of medical drugs and co-morbid medical conditions may the leading cause of the inconsistent results. In our study, increased FA was found in parietal-frontal white matter in the chronic cigarette smokers relative to healthy non-smokers. This discrepancy might arise from sample differences, such as differences in ethnicity, levels of cigarette smoking , age and psychiatric comorbidity . Results of our study indicate that the maintenance of cigarette smoking might involve fronto-parietal circuitry. Scientific evidence indicaties that the fronto-parietal cortex is one of the crucial units that functionally connects interrelated brain regions. Dosenbach et al indicated that this fronto-parietal circuitry initiates and adjusts control. There is also evidence that there is a network of frontal and parietal areas, which shows significant interactions between changes to a particular stimulus dimension and the current focus of attention.

Findings from a previous study suggest that during nicotine withdrawal, functional integration of fronto-parietal networks is abnormal in cannabis users. Previous studies and our results may indicate altered connectivity within a cognitive network that is mediated by abnormal neurogenic functional activation in chronic nicotine exposure. In order to fully understand the mechanism of structural alteration in fronto-parietal cortex of chronic smoking, further studies using techniques such as adaptation or multi-voxel pattern analysis will be needed. A number of limitations to our study should be addressed. First of all, possible sex differences in the response to nicotine may exist. We did not evaluate sex differences because of the relatively small number of female participants, which is a limitation of the study, although we matched for the gender proportion between smokers and healthy non-smokers. Second, education level was significant ly higher in the nonsmoker group compared to the smoker group. However, when we explicitly explored the impact of education level on bilateral fronto-parietal white matter in the smoker group, we found no significant correlation . This suggests that our findings cannot simply be explained in terms of this variable. In conclusion, our DTI data further support the hypothesis that smokers and non-smokers differed in bilateral fronto-parietal white matter integrity. These findings support the hypothesis that chronic cigarette smoking involves alteration of fronto-parietal connectivity.The homeless population is aging . People born in the second half of the “baby‐boom” have an elevated risk of homelessness . Homeless adults develop aging‐related conditions, including functional impairment, earlier than individuals in the general population. For this reason, homeless adults aged 50 and older are considered “older” despite their relatively young age . The homeless population has a higher prevalence of mental health and substance use problems than the general population . Individuals experiencing homelessness report barriers to mental health services, due to lack of insurance coverage, high cost of care, and inability to identify sources of care . These barriers can prevent their using services to treat mental health and substance use problems, such as outpatient counseling, prescription medication, and community‐based substance use treatment.

Without these, homeless populations may experience more severe behavioral health problems and rely on acute care to address these chronic conditions. Homeless individuals have higher rates of Emergency Department use for mental health and substance use concerns , and are more likely to use psychiatric inpatient or ED services and less likely to use outpatient treatment than those who are housed . Homeless adults with substance use disorders face multiple barriers to engaging in substance use treatment. Competing needs , financial concerns, lack of knowledge about or connection to available services, and lack of insurance are barriers to substance use treatment among homeless adults . Older adults face additional barriers to mental health or substance use treatment due to cognitive and functional impairment, such as difficulty navigating and traveling to healthcare systems . However, there is little known about older adults experiencing homelessness. According to Gelberg and Anderson’s Behavioral Model for Vulnerable Populations, predisposing factors, enabling factors, and need,garden racks wholesale shape health care utilization . Although prior research has used this model for homeless populations, this work has not included older homeless adults . Little is known about the prevalence of mental health or substance use problems in older homeless adults, the level of unmet need for services, or the factors associated with that need. To understand the factors associated with unmet need for mental health and substance use treatment in older homeless adults, in a population‐based sample of homeless adults age 50 and older, we identified those with a need for mental health and substance use services. Then, we applied the Gelberg and Anderson model to examine predisposing and enabling factors associated with unmet need, which we defined as not receiving mental health and substance use treatment among participants with mental health or substance use problems .We defined having a need for mental health treatment by having a positive screen for depressive symptoms or post traumatic stress disorder symptoms or reporting symptoms of other mental health problems, including anxiety, hallucinations, thoughts of suicide, or attempted suicide in the past 6 months. To assess current depressive symptoms, we used the Center for Epidemiologic Studies Depression Scale , considering a score of ≥22 to be evidence of depressive symptoms . We evaluated current PTSD symptoms using the Primary Care PTSD Screen , which asks participants to report whether they experienced any of four symptoms in the previous month due to a past experience: nightmares, avoidance of situations that reminded them of it, hypervigilance, or emotional numbing to their surroundings . We considered a score of four to be consistent with PTSD symptoms. To assess additional mental health problems , we used questions from the National Survey of Homeless Assistance Providers and Clients , as adapted from the Addiction Severity Index  and considered a report of any of those symptoms to be evidence of other mental health problems. We considered anyone who met criteria for depressive symptoms, PTSD symptoms or other mental health problems to have a mental health need.Drawing on Gelberg and Anderson’s model, we examined factors associated with not having received mental health treatment among those with a mental health need . We included the factors listed above, which we identified a priori. In the model with unmet need for mental health services, we examined whether having an alcohol or drug use problem was associated with unmet need, considering them to be need factors . We conducted a separate analysis to examine factors associated with not having received substance use treatment amongst those with an identified need; we again used the Gelberg and Anderson model and used factors listed above, which we identified factors a priori. In the substance use model, we tested whether having depressive symptoms, PTSD symptoms, or additional mental health problems, conceptualized as need factors, were associated .

We used logistic regression in these analyses. To construct our models, we included only hypothesized variables with a bivariate p value of <0.20 in the full multivariate model. To define our reduced model, we conducted backward elimination, retaining independent variables with p ≤ .05. Due to a skip pattern error, we incorrectly assessed 33 individuals using the AUDIT. To correct for this, we used multiple imputation to estimate the relationship between the treatment variables and the total AUDIT scores. We conducted multiple imputation analysis in STATA 14.2 . We used SAS 9.4 to conduct our descriptive and logistic regression analyses. In a population‐based sample of older adults experiencing homelessness, we found a high prevalence of unmet need for mental health and substance use treatment. While the majority of participants had mental health and substance use problems, few received treatment. One‐third of those with mental health need received mental health care. Fewer than 13% of those with substance use need received substance use treatment. We identified predisposing and enabling factors associated with unmet treatment need. Adults aged 65 and over had a higher odds of unmet need for mental health treatment. Older adults are more likely to have competing demands, including higher physical health needs, which can interfere with receiving behavioral healthcare . Due to a shortage of geriatric psychiatrists and geriatric mental health care services, older adults may not have access to treatment when they seek care . The homeless population age 65 and older is expected to triple by the year 2020 . Thus, there is a need to design care that meets the needs of this growing, but underserved, population. We found that having a regular healthcare provider was associated with less unmet need. Having a regular provider can increase engagement because primary care providers may help identify needs and refer to care. In safety‐net systems, such as the ones in which our participants receive care, primary care providers may be the primary source of mental health treatment, by prescribing psychotropic medication. Primary care providers are responsible for an increasing proportion of prescriptions for psychotropic medication . In addition to prescribing medication for mental health conditions, primary care providers can refer patients to outpatient mental health counseling and treatment with specialist staff or providers. In some safety‐net settings, mental health services may be colocated with physical health services via collaborative care models.Collaborative care models can enhance information sharing and treatment plan collaboration and reduce barriers to care .CCMs are cost‐efficient and can increase the capacity of resource‐constrained settings to provide care for patients with complex needs . Federally Qualified Health Centers can bill for both a medical and mental health visit on the same day , and recent changes to FQHC payment codes allow billing for behavioral health care management services in addition to the FQHC billable visit. Pay‐for‐performance programs link public hospitals’ payments to care coordination and mental health treatment metrics .